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Sample records for robot learning je

Memory and learning seems to be related to cholinergic dysfunction in the JE rat model.

Science.gov (United States)

Chauhan, Prashant Singh; Misra, Usha Kant; Kalita, Jayantee; Chandravanshi, Lalit Pratap; Khanna, Vinay Kumar

2016-03-15

Cognitive changes have been known in encephalitis but in Japanese encephalitis (JE) such studies are limited. This study aims at evaluating the spatial memory and learning and correlate with markers of cholinergic activity in the brain.12day old Wistar rats were inoculated with dose of 3×10(6)pfu/ml of JE virus. On 10, 33 and 48days post-inoculation (dpi), spatial memory and learning was assessed by Y maze. Brain biopsies from frontal cortex, corpus striatum, hippocampus and cerebellum were taken. Muscarinic cholinergic receptor was assayed by Quinuclidinyl benzylate (H3-QNB) binding, CHRM2 gene expression by real time PCR and choline acetyl transferase (ChAT) by Western blot. Spatial learning and memory showed significant decline in rats inoculated with JEV on 10 and 33dpi (47.5%, pmemory were found at different dpi. There was transient spatial learning and memory impairment which was associated with reduction of total muscarinic receptor binding, CHRM2 gene and ChAT expression in different brain region of rat infected with JE Virus. Copyright © 2016 Elsevier Inc. All rights reserved.
Learning for intelligent mobile robots

Science.gov (United States)

Hall, Ernest L.; Liao, Xiaoqun; Alhaj Ali, Souma M.

2003-10-01

Unlike intelligent industrial robots which often work in a structured factory setting, intelligent mobile robots must often operate in an unstructured environment cluttered with obstacles and with many possible action paths. However, such machines have many potential applications in medicine, defense, industry and even the home that make their study important. Sensors such as vision are needed. However, in many applications some form of learning is also required. The purpose of this paper is to present a discussion of recent technical advances in learning for intelligent mobile robots. During the past 20 years, the use of intelligent industrial robots that are equipped not only with motion control systems but also with sensors such as cameras, laser scanners, or tactile sensors that permit adaptation to a changing environment has increased dramatically. However, relatively little has been done concerning learning. Adaptive and robust control permits one to achieve point to point and controlled path operation in a changing environment. This problem can be solved with a learning control. In the unstructured environment, the terrain and consequently the load on the robot"s motors are constantly changing. Learning the parameters of a proportional, integral and derivative controller (PID) and artificial neural network provides an adaptive and robust control. Learning may also be used for path following. Simulations that include learning may be conducted to see if a robot can learn its way through a cluttered array of obstacles. If a situation is performed repetitively, then learning can also be used in the actual application. To reach an even higher degree of autonomous operation, a new level of learning is required. Recently learning theories such as the adaptive critic have been proposed. In this type of learning a critic provides a grade to the controller of an action module such as a robot. The creative control process is used that is "beyond the adaptive critic." A
Framework for robot skill learning using reinforcement learning

Science.gov (United States)

Wei, Yingzi; Zhao, Mingyang

2003-09-01

Robot acquiring skill is a process similar to human skill learning. Reinforcement learning (RL) is an on-line actor critic method for a robot to develop its skill. The reinforcement function has become the critical component for its effect of evaluating the action and guiding the learning process. We present an augmented reward function that provides a new way for RL controller to incorporate prior knowledge and experience into the RL controller. Also, the difference form of augmented reward function is considered carefully. The additional reward beyond conventional reward will provide more heuristic information for RL. In this paper, we present a strategy for the task of complex skill learning. Automatic robot shaping policy is to dissolve the complex skill into a hierarchical learning process. The new form of value function is introduced to attain smooth motion switching swiftly. We present a formal, but practical, framework for robot skill learning and also illustrate with an example the utility of method for learning skilled robot control on line.
Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

Science.gov (United States)

de Greeff, Joachim; Belpaeme, Tony

2015-01-01

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.
Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

Directory of Open Access Journals (Sweden)

Joachim de Greeff

Full Text Available Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference; the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.
Robots Learn Writing

Directory of Open Access Journals (Sweden)

Huan Tan

2012-01-01

Full Text Available This paper proposes a general method for robots to learn motions and corresponding semantic knowledge simultaneously. A modified ISOMAP algorithm is used to convert the sampled 6D vectors of joint angles into 2D trajectories, and the required movements for writing numbers are learned from this modified ISOMAP-based model. Using this algorithm, the knowledge models are established. Learned motion and knowledge models are stored in a 2D latent space. Gaussian Process (GP method is used to model and represent these models. Practical experiments are carried out on a humanoid robot, named ISAC, to learn the semantic representations of numbers and the movements of writing numbers through imitation and to verify the effectiveness of this framework. This framework is applied into training a humanoid robot, named ISAC. At the learning stage, ISAC not only learns the dynamics of the movement required to write the numbers, but also learns the semantic meaning of the numbers which are related to the writing movements from the same data set. Given speech commands, ISAC recognizes the words and generated corresponding motion trajectories to write the numbers. This imitation learning method is implemented on a cognitive architecture to provide robust cognitive information processing.
Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction

Science.gov (United States)

de Greeff, Joachim; Belpaeme, Tony

2015-01-01

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children’s social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a “mental model” of the robot, tailoring the tutoring to the robot’s performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot’s bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance. PMID:26422143
Machine Learning for Robotic Vision

OpenAIRE

Drummond, Tom

2018-01-01

Machine learning is a crucial enabling technology for robotics, in particular for unlocking the capabilities afforded by visual sensing. This talk will present research within Prof Drummond’s lab that explores how machine learning can be developed and used within the context of Robotic Vision.
Learning ROS for robotics programming

CERN Document Server

Martinez, Aaron

2013-01-01

The book will take an easy-to-follow and engaging tutorial approach, providing a practical and comprehensive way to learn ROS.If you are a robotic enthusiast who wants to learn how to build and program your own robots in an easy-to-develop, maintainable and shareable way, ""Learning ROS for Robotics Programming"" is for you. In order to make the most of the book, you should have some C++ programming background, knowledge of GNU/Linux systems, and computer science in general. No previous background on ROS is required, since this book provides all the skills required. It is also advisable to hav
Morphology Independent Learning in Modular Robots

DEFF Research Database (Denmark)

Christensen, David Johan; Bordignon, Mirko; Schultz, Ulrik Pagh

2009-01-01

Hand-coding locomotion controllers for modular robots is difﬁcult due to their polymorphic nature. Instead, we propose to use a simple and distributed reinforcement learning strategy. ATRON modules with identical controllers can be assembled in any conﬁguration. To optimize the robot’s locomotion...... speed its modules independently and in parallel adjust their behavior based on a single global reward signal. In simulation, we study the learning strategy’s performance on different robot conﬁgurations. On the physical platform, we perform learning experiments with ATRON robots learning to move as fast...
Morphology Independent Learning in Modular Robots

DEFF Research Database (Denmark)

Christensen, David Johan; Bordignon, Mirko; Schultz, Ulrik Pagh

2009-01-01

speed its modules independently and in parallel adjust their behavior based on a single global reward signal. In simulation, we study the learning strategy’s performance on different robot conﬁgurations. On the physical platform, we perform learning experiments with ATRON robots learning to move as fast...
Morphology Independent Learning in Modular Robots

DEFF Research Database (Denmark)

Christensen, David Johan; Bordignon, Mirko; Schultz, Ulrik Pagh

2009-01-01

speed its modules independently and in parallel adjust their behavior based on a single global reward signal. In simulation, we study the learning strategy?s performance on different robot con?gurations. On the physical platform, we perform learning experiments with ATRON robots learning to move as fast...
Robot Learning a New Subfield? The Robolearn-96 Workshop

OpenAIRE

Hexmoor, Henry; Meeden, Lisa; Murphy, Robin R.

1997-01-01

This article posits the idea of robot learning as a new subfield. The results of the Robolearn-96 Workshop provide evidence that learning in modern robotics is distinct from traditional machine learning. The article examines the role of robotics in the social and natural sciences and the potential impact of learning on robotics, generating both a continuum of research issues and a description of the divergent terminology, target domains, and standards of proof associated with robot learning. ...
Robot learning and error correction

Science.gov (United States)

Friedman, L.

1977-01-01

A model of robot learning is described that associates previously unknown perceptions with the sensed known consequences of robot actions. For these actions, both the categories of outcomes and the corresponding sensory patterns are incorporated in a knowledge base by the system designer. Thus the robot is able to predict the outcome of an action and compare the expectation with the experience. New knowledge about what to expect in the world may then be incorporated by the robot in a pre-existing structure whether it detects accordance or discrepancy between a predicted consequence and experience. Errors committed during plan execution are detected by the same type of comparison process and learning may be applied to avoiding the errors.
Serendipitous Offline Learning in a Neuromorphic Robot

Directory of Open Access Journals (Sweden)

Terrence C Stewart

2016-02-01

Full Text Available We demonstrate a hybrid neuromorphic learning paradigm that learns complex sensorimotor mappings based on a small set of hard-coded reflex behaviours. A mobile robot is first controlled by a basic set of reflexive hand-designed behaviours. All sensor data is provided via a spike-based silicon retina camera (eDVS, and all control is implemented via spiking neurons simulated on neuromorphic hardware (SpiNNaker. Given this control system, the robot is capable of simple obstacle avoidance and random exploration. To train the robot to perform more complex tasks, we observe the robot and find instances where he robot accidentally performs the desired action. Data recorded from the robot during these times is then used to update the neural control system, increasing the likelihood of the robot performing that task in the future, given a similar sensor state. As an example application of this general-purpose method of training, we demonstrate the robot learning to respond to novel sensory stimuli (a mirror by turning right if it is present at an intersection, and otherwise turning left. In general, this system can learn arbitrary relations between sensory input and motor behaviour.
Memristive device based learning for navigation in robots.

Science.gov (United States)

Sarim, Mohammad; Kumar, Manish; Jha, Rashmi; Minai, Ali A

2017-11-08

Biomimetic robots have gained attention recently for various applications ranging from resource hunting to search and rescue operations during disasters. Biological species are known to intuitively learn from the environment, gather and process data, and make appropriate decisions. Such sophisticated computing capabilities in robots are difficult to achieve, especially if done in real-time with ultra-low energy consumption. Here, we present a novel memristive device based learning architecture for robots. Two terminal memristive devices with resistive switching of oxide layer are modeled in a crossbar array to develop a neuromorphic platform that can impart active real-time learning capabilities in a robot. This approach is validated by navigating a robot vehicle in an unknown environment with randomly placed obstacles. Further, the proposed scheme is compared with reinforcement learning based algorithms using local and global knowledge of the environment. The simulation as well as experimental results corroborate the validity and potential of the proposed learning scheme for robots. The results also show that our learning scheme approaches an optimal solution for some environment layouts in robot navigation.
Sva je slatka

Directory of Open Access Journals (Sweden)

Ana Stanić

2017-06-01

Full Text Available Izgleda tako slatko, ali živi je otrov. Pogledajmo malo dvoranu: svi su unutra. Trećini njih, a i to sam malo rekao, nisam baš drag. Sve su to moji protivnici, moji krvnici i moje žrtve. Petnaest sam godina u firmi, zadnjih pet kao šef kadrovske – nije lako. Ali od svih tih dama i gospode koji me mrze, vrlo dobro znam, najgora je ona. Moja najveća neprijateljica. Znam što govorim jer dobro je poznajem: to mi je žena.A konkurencija je jaka, tu su svi moji najratoborniji, najžilaviji protivnici: Donatella, ekonomistica s magisterijem s Harvarda, koju sam zaposlio kao tajnicu kad zbog krize nije mogla naći posao i koja mi je jednom prigodom, lagano i namjerno, izlila kipuću kavu po hlačama jer sam je zamolio da nam na sastanak direktora donese piće (A što sam mogao? Nisam ja kriv za krizu. A na sastanku je bio generalni direktor. I lijepo sam je zamolio. Zaldíbar, koji se iživljavao nada mnom šest godina kad mi je bio šef, potpisujući – bez moga znanja – sva moja izvješća kao svoja. Contreras, koji je pucao na moje mjesto, ali je izgubio bitku, čemu je vjerojatno pripomoglo to što sam se ja slučajno učlanio u isti teniski klub kao i generalni direktor, s kojim sam se uspio sprijateljiti između dva forehanda (nisam ni ja svetac, ali nisam ni đubre kao Zaldíbar – recimo da je to normalna, uobičajena doza nečasna ponašanja. Dakle, i u društvu ta tri teškaša, ona mi je i dalje najveća neprijateljica, u toj dvorani i u svemiru. To što smo u braku samo pogoršava stvari. Spavam s njom, sa svojom smrtnom neprijateljicom, a kad ne mogu spavati, čini mi se da je čujem kako u tišini snuje tajne osvetničke planove.
A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning.

Directory of Open Access Journals (Sweden)

Michael Jae-Yoon Chung

Full Text Available A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i learn probabilistic models of actions through self-discovery and experience, (ii utilize these learned models for inferring the goals of human actions, and (iii perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i a simulated robot that learns human-like gaze following behavior, and (ii a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.
A Semi-Open Learning Environment for Mobile Robotics

Directory of Open Access Journals (Sweden)

Enrique Sucar

2007-05-01

Full Text Available We have developed a semi-open learning environment for mobile robotics, to learn through free exploration, but with specific performance criteria that guides the learning process. The environment includes virtual and remote robotics laboratories, and an intelligent virtual assistant the guides the students using the labs. A series of experiments in the virtual and remote labs are designed to gradually learn the basics of mobile robotics. Each experiment considers exploration and performance aspects, which are evaluated by the virtual assistant, giving feedback to the user. The virtual laboratory has been incorporated to a course in mobile robotics and used by a group of students. A preliminary evaluation shows that the intelligent tutor combined with the virtual laboratory can improve the learning process.
ROBOT LEARNING OF OBJECT MANIPULATION TASK ACTIONS FROM HUMAN DEMONSTRATIONS

Directory of Open Access Journals (Sweden)

Maria Kyrarini

2017-08-01

Full Text Available Robot learning from demonstration is a method which enables robots to learn in a similar way as humans. In this paper, a framework that enables robots to learn from multiple human demonstrations via kinesthetic teaching is presented. The subject of learning is a high-level sequence of actions, as well as the low-level trajectories necessary to be followed by the robot to perform the object manipulation task. The multiple human demonstrations are recorded and only the most similar demonstrations are selected for robot learning. The high-level learning module identifies the sequence of actions of the demonstrated task. Using Dynamic Time Warping (DTW and Gaussian Mixture Model (GMM, the model of demonstrated trajectories is learned. The learned trajectory is generated by Gaussian mixture regression (GMR from the learned Gaussian mixture model. In online working phase, the sequence of actions is identified and experimental results show that the robot performs the learned task successfully.

Autonomous Motion Learning for Intra-Vehicular Activity Space Robot

Science.gov (United States)

Watanabe, Yutaka; Yairi, Takehisa; Machida, Kazuo

Space robots will be needed in the future space missions. So far, many types of space robots have been developed, but in particular, Intra-Vehicular Activity (IVA) space robots that support human activities should be developed to reduce human-risks in space. In this paper, we study the motion learning method of an IVA space robot with the multi-link mechanism. The advantage point is that this space robot moves using reaction force of the multi-link mechanism and contact forces from the wall as space walking of an astronaut, not to use a propulsion. The control approach is determined based on a reinforcement learning with the actor-critic algorithm. We demonstrate to clear effectiveness of this approach using a 5-link space robot model by simulation. First, we simulate that a space robot learn the motion control including contact phase in two dimensional case. Next, we simulate that a space robot learn the motion control changing base attitude in three dimensional case.
Robot learning from human teachers

CERN Document Server

Chernova, Sonia

2014-01-01

Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn f
Robots show us how to teach them: feedback from robots shapes tutoring behavior during action learning.

Science.gov (United States)

Vollmer, Anna-Lisa; Mühlig, Manuel; Steil, Jochen J; Pitsch, Karola; Fritsch, Jannik; Rohlfing, Katharina J; Wrede, Britta

2014-01-01

Robot learning by imitation requires the detection of a tutor's action demonstration and its relevant parts. Current approaches implicitly assume a unidirectional transfer of knowledge from tutor to learner. The presented work challenges this predominant assumption based on an extensive user study with an autonomously interacting robot. We show that by providing feedback, a robot learner influences the human tutor's movement demonstrations in the process of action learning. We argue that the robot's feedback strongly shapes how tutors signal what is relevant to an action and thus advocate a paradigm shift in robot action learning research toward truly interactive systems learning in and benefiting from interaction.
Robot soccer action selection based on Q learning

Institute of Scientific and Technical Information of China (English)

无

2001-01-01

This paper researches robot soccer action selection based on Q learning . The robot learn to activate particular behavior given their current situation and reward signal. We adopt neural network to implementations of Q learning for their generalization properties and limited computer memory requirements
Can young children learn words from a robot?

OpenAIRE

Moriguchi, Yusuke; Kanda, Takayuki; Ishiguro, Hiroshi; Shimada, Yoko; Itakura, Shoji

2011-01-01

Young children generally learn words from other people. Recent research has shown that children can learn new actions and skills from nonhuman agents. This study examines whether young children could learn words from a robot. Preschool children were shown a video in which either a woman (human condition) or a mechanical robot (robot condition) labeled novel objects. Then the children were asked to select the objects according to the names used in the video. The results revealed that children ...
Learning motor skills from algorithms to robot experiments

CERN Document Server

Kober, Jens

2014-01-01

This book presents the state of the art in reinforcement learning applied to robotics both in terms of novel algorithms and applications. It discusses recent approaches that allow robots to learn motor skills and presents tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters, and appropriate kernel-based reinforcement learning algorithms. The presented applications explore highly dynamic tasks and exhibit a very efficient learning process. All proposed approaches have been extensively validated with benchmarks tasks, in simulation, and on real robots. These tasks correspond to sports and games but the presented techniques are also applicable to more mundane household tasks. The book is based on the first author’s doctoral thesis, which wo...
Learning Semantics of Gestural Instructions for Human-Robot Collaboration

Science.gov (United States)

Shukla, Dadhichi; Erkent, Özgür; Piater, Justus

2018-01-01

Designed to work safely alongside humans, collaborative robots need to be capable partners in human-robot teams. Besides having key capabilities like detecting gestures, recognizing objects, grasping them, and handing them over, these robots need to seamlessly adapt their behavior for efficient human-robot collaboration. In this context we present the fast, supervised Proactive Incremental Learning (PIL) framework for learning associations between human hand gestures and the intended robotic manipulation actions. With the proactive aspect, the robot is competent to predict the human's intent and perform an action without waiting for an instruction. The incremental aspect enables the robot to learn associations on the fly while performing a task. It is a probabilistic, statistically-driven approach. As a proof of concept, we focus on a table assembly task where the robot assists its human partner. We investigate how the accuracy of gesture detection affects the number of interactions required to complete the task. We also conducted a human-robot interaction study with non-roboticist users comparing a proactive with a reactive robot that waits for instructions. PMID:29615888
Learning Semantics of Gestural Instructions for Human-Robot Collaboration.

Science.gov (United States)

Shukla, Dadhichi; Erkent, Özgür; Piater, Justus

2018-01-01

Designed to work safely alongside humans, collaborative robots need to be capable partners in human-robot teams. Besides having key capabilities like detecting gestures, recognizing objects, grasping them, and handing them over, these robots need to seamlessly adapt their behavior for efficient human-robot collaboration. In this context we present the fast, supervised Proactive Incremental Learning (PIL) framework for learning associations between human hand gestures and the intended robotic manipulation actions. With the proactive aspect, the robot is competent to predict the human's intent and perform an action without waiting for an instruction. The incremental aspect enables the robot to learn associations on the fly while performing a task. It is a probabilistic, statistically-driven approach. As a proof of concept, we focus on a table assembly task where the robot assists its human partner. We investigate how the accuracy of gesture detection affects the number of interactions required to complete the task. We also conducted a human-robot interaction study with non-roboticist users comparing a proactive with a reactive robot that waits for instructions.
Adaptive learning fuzzy control of a mobile robot

International Nuclear Information System (INIS)

Tsukada, Akira; Suzuki, Katsuo; Fujii, Yoshio; Shinohara, Yoshikuni

1989-11-01

In this report a problem is studied to construct a fuzzy controller for a mobile robot to move autonomously along a given reference direction curve, for which control rules are generated and acquired through an adaptive learning process. An adaptive learning fuzzy controller has been developed for a mobile robot. Good properties of the controller are shown through the travelling experiments of the mobile robot. (author)
Neural Behavior Chain Learning of Mobile Robot Actions

Directory of Open Access Journals (Sweden)

Lejla Banjanovic-Mehmedovic

2012-01-01

Full Text Available This paper presents a visual/motor behavior learning approach, based on neural networks. We propose Behavior Chain Model (BCM in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are derived. Second, a multilayer neural network for offline learning of the mapping relations is used. This learning structure through neural network training process represents a connection between the visual perceptions and motor sequence of actions in order to grip a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot behavior for a variety of similar tasks in similar environment. Prediction results suggest that the methodology is adequate and could be recognized as an idea for designing different mobile robot behaviour assistance.
Approaches to probabilistic model learning for mobile manipulation robots

CERN Document Server

Sturm, Jürgen

2013-01-01

Mobile manipulation robots are envisioned to provide many useful services both in domestic environments as well as in the industrial context. Examples include domestic service robots that implement large parts of the housework, and versatile industrial assistants that provide automation, transportation, inspection, and monitoring services. The challenge in these applications is that the robots have to function under changing, real-world conditions, be able to deal with considerable amounts of noise and uncertainty, and operate without the supervision of an expert. This book presents novel learning techniques that enable mobile manipulation robots, i.e., mobile platforms with one or more robotic manipulators, to autonomously adapt to new or changing situations. The approaches presented in this book cover the following topics: (1) learning the robot's kinematic structure and properties using actuation and visual feedback, (2) learning about articulated objects in the environment in which the robot is operating,...
Continuing Robot Skill Learning after Demonstration with Human Feedback

Directory of Open Access Journals (Sweden)

Argall Brenna D.

2011-12-01

Full Text Available Though demonstration-based approaches have been successfully applied to learning a variety of robot behaviors, there do exist some limitations. The ability to continue learning after demonstration, based on execution experience with the learned policy, therefore has proven to be an asset to many demonstration-based learning systems. This paper discusses important considerations for interfaces that provide feedback to adapt and improve demonstrated behaviors. Feedback interfaces developed for two robots with very different motion capabilities - a wheeled mobile robot and high degree-of-freedom humanoid - are highlighted.
Human likeness: cognitive and affective factors affecting adoption of robot-assisted learning systems

Science.gov (United States)

Yoo, Hosun; Kwon, Ohbyung; Lee, Namyeon

2016-07-01

With advances in robot technology, interest in robotic e-learning systems has increased. In some laboratories, experiments are being conducted with humanoid robots as artificial tutors because of their likeness to humans, the rich possibilities of using this type of media, and the multimodal interaction capabilities of these robots. The robot-assisted learning system, a special type of e-learning system, aims to increase the learner's concentration, pleasure, and learning performance dramatically. However, very few empirical studies have examined the effect on learning performance of incorporating humanoid robot technology into e-learning systems or people's willingness to accept or adopt robot-assisted learning systems. In particular, human likeness, the essential characteristic of humanoid robots as compared with conventional e-learning systems, has not been discussed in a theoretical context. Hence, the purpose of this study is to propose a theoretical model to explain the process of adoption of robot-assisted learning systems. In the proposed model, human likeness is conceptualized as a combination of media richness, multimodal interaction capabilities, and para-social relationships; these factors are considered as possible determinants of the degree to which human cognition and affection are related to the adoption of robot-assisted learning systems.
TEXPLORE temporal difference reinforcement learning for robots and time-constrained domains

CERN Document Server

Hester, Todd

2013-01-01

This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuou...
HCBPM: An Idea toward a Social Learning Environment for Humanoid Robot

Directory of Open Access Journals (Sweden)

Fady Alnajjar

2010-01-01

Full Text Available To advance robotics toward real-world applications, a growing body of research has focused on the development of control systems for humanoid robots in recent years. Several approaches have been proposed to support the learning stage of such controllers, where the robot can learn new behaviors by observing and/or receiving direct guidance from a human or even another robot. These approaches require dynamic learning and memorization techniques, which the robot can use to reform and update its internal systems continuously while learning new behaviors. Against this background, this study investigates a new approach to the development of an incremental learning and memorization model. This approach was inspired by the principles of neuroscience, and the developed model was named “Hierarchical Constructive Backpropagation with Memory” (HCBPM. The validity of the model was tested by teaching a humanoid robot to recognize a group of objects through natural interaction. The experimental results indicate that the proposed model efficiently enhances real-time machine learning in general and can be used to establish an environment suitable for social learning between the robot and the user in particular.
Exploration and Learning for Cognitive Robots

NARCIS (Netherlands)

Rudinac, M.

2013-01-01

Before a future with household robots is really feasible, those robots need to be easily adaptable to novel environments and users, be able to apply previously acquired knowledge, and able to learn from perceiving and interacting with the world and users around them. This thesis proposes a cognitive
Experiential Learning of Robotics Fundamentals Based on a Case Study of Robot-Assisted Stereotactic Neurosurgery

Science.gov (United States)

Faria, Carlos; Vale, Carolina; Machado, Toni; Erlhagen, Wolfram; Rito, Manuel; Monteiro, Sérgio; Bicho, Estela

2016-01-01

Robotics has been playing an important role in modern surgery, especially in procedures that require extreme precision, such as neurosurgery. This paper addresses the challenge of teaching robotics to undergraduate engineering students, through an experiential learning project of robotics fundamentals based on a case study of robot-assisted…
Fable II: Design of a Modular Robot for Creative Learning

DEFF Research Database (Denmark)

Pacheco, Moises; Fogh, Rune; Lund, Henrik Hautop

2015-01-01

Robotic systems have a high potential for creative learning if they are flexible, accessible and engaging for the user in the experimental process of building and programming robots. In this paper we describe the Fable modular robotic system for creative learning which we develop to enable and mo...
Intrinsically motivated reinforcement learning for human-robot interaction in the real-world.

Science.gov (United States)

Qureshi, Ahmed Hussain; Nakamura, Yutaka; Yoshikawa, Yuichiro; Ishiguro, Hiroshi

2018-03-26

For a natural social human-robot interaction, it is essential for a robot to learn the human-like social skills. However, learning such skills is notoriously hard due to the limited availability of direct instructions from people to teach a robot. In this paper, we propose an intrinsically motivated reinforcement learning framework in which an agent gets the intrinsic motivation-based rewards through the action-conditional predictive model. By using the proposed method, the robot learned the social skills from the human-robot interaction experiences gathered in the real uncontrolled environments. The results indicate that the robot not only acquired human-like social skills but also took more human-like decisions, on a test dataset, than a robot which received direct rewards for the task achievement. Copyright © 2018 Elsevier Ltd. All rights reserved.
Safe robot execution in model-based reinforcement learning

OpenAIRE

Martínez Martínez, David; Alenyà Ribas, Guillem; Torras, Carme

2015-01-01

Task learning in robotics requires repeatedly executing the same actions in different states to learn the model of the task. However, in real-world domains, there are usually sequences of actions that, if executed, may produce unrecoverable errors (e.g. breaking an object). Robots should avoid repeating such errors when learning, and thus explore the state space in a more intelligent way. This requires identifying dangerous action effects to avoid including such actions in the generated plans...

Robotic Fish to Aid Animal Behavior Studies and Informal Science Learning

Science.gov (United States)

Phamduy, Paul

The application of robotic fish in the fields of animal behavior and informal science learning are new and relatively untapped. In the context of animal behavior studies, robotic fish offers a consistent and customizable stimulus that could contribute to dissect the determinants of social behavior. In the realm of informal science learning, robotic fish are gaining momentum for the possibility of educating the general public simultaneously on fish physiology and underwater robotics. In this dissertation, the design and development of a number of robotic fish platforms and prototypes and their application in animal behavioral studies and informal science learning settings are presented. Robotic platforms for animal behavioral studies focused on the utilization replica or same scale prototypes. A novel robotic fish platform, featuring a three-dimensional swimming multi-linked robotic fish, was developed with three control modes varying in the level of robot autonomy offered. This platform was deployed at numerous science festivals and science centers, to obtain data on visitor engagement and experience.
The Robotic Decathlon: Project-Based Learning Labs and Curriculum Design for an Introductory Robotics Course

Science.gov (United States)

Cappelleri, D. J.; Vitoroulis, N.

2013-01-01

This paper presents a series of novel project-based learning labs for an introductory robotics course that are developed into a semester-long Robotic Decathlon. The last three events of the Robotic Decathlon are used as three final one-week-long project tasks; these replace a previous course project that was a semester-long robotics competition.…
Educational resources and tools for robotic learning

Directory of Open Access Journals (Sweden)

Pablo Gil Vazquez

2012-07-01

Full Text Available Normal.dotm 0 0 1 139 795 Universidad de Salamanca 6 1 976 12.0 0 false 18 pt 18 pt 0 0 false false false /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} This paper discusses different teaching experiences which aims are the learning robotics in the university. These experiences are reflected in the development of several robotics courses and subjects at the University of Alicante. The authors have created various educational platforms or they have used tools of free distribution and open source for the implementation of these courses. The main objetive of these courses is to teach the design and implementation of robotic solutions to solve various problems not only such as the control, programming and handling of robot but also the assembly, building and programming of educational mini-robots. On the one hand, new teaching tools are used such as simulators and virtual labs which make flexible the learning of robot arms. On the other hand, competitions are used to motivate students because this way, the students put into action the skills learned through building and programming low-cost mini-robots.
Experiential Learning of Electronics Subject Matter in Middle School Robotics Courses

Science.gov (United States)

Rihtaršic, David; Avsec, Stanislav; Kocijancic, Slavko

2016-01-01

The purpose of this paper is to investigate whether the experiential learning of electronics subject matter is effective in the middle school open learning of robotics. Electronics is often ignored in robotics courses. Since robotics courses are typically comprised of computer-related subjects, and mechanical and electrical engineering, these…
Project InterActions: A Multigenerational Robotic Learning Environment

Science.gov (United States)

Bers, Marina U.

2007-12-01

This paper presents Project InterActions, a series of 5-week workshops in which very young learners (4- to 7-year-old children) and their parents come together to build and program a personally meaningful robotic project in the context of a multigenerational robotics-based community of practice. The goal of these family workshops is to teach both parents and children about the mechanical and programming aspects involved in robotics, as well as to initiate them in a learning trajectory with and about technology. Results from this project address different ways in which parents and children learn together and provide insights into how to develop educational interventions that would educate parents, as well as children, in new domains of knowledge and skills such as robotics and new technologies.
Structured Kernel Subspace Learning for Autonomous Robot Navigation.

Science.gov (United States)

Kim, Eunwoo; Choi, Sungjoon; Oh, Songhwai

2018-02-14

This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.
A Project-based Learning approach for teaching Robotics to ...

African Journals Online (AJOL)

In this research we used a project-based learning approach to teach robotics basics to undergraduate business computing students. The course coverage includes basic electronics, robot construction and programming using arduino. Students developed and tested a robot prototype. The project was evaluated using a ...
Service Oriented Robotic Architecture for Space Robotics: Design, Testing, and Lessons Learned

Science.gov (United States)

Fluckiger, Lorenzo Jean Marc E; Utz, Hans Heinrich

2013-01-01

This paper presents the lessons learned from six years of experiments with planetary rover prototypes running the Service Oriented Robotic Architecture (SORA) developed by the Intelligent Robotics Group (IRG) at the NASA Ames Research Center. SORA relies on proven software engineering methods and technologies applied to space robotics. Based on a Service Oriented Architecture and robust middleware, SORA encompasses on-board robot control and a full suite of software tools necessary for remotely operated exploration missions. SORA has been eld tested in numerous scenarios of robotic lunar and planetary exploration. The experiments conducted by IRG with SORA exercise a large set of the constraints encountered in space applications: remote robotic assets, ight relevant science instruments, distributed operations, high network latencies and unreliable or intermittent communication links. In this paper, we present the results of these eld tests in regard to the developed architecture, and discuss its bene ts and limitations.
Learning compliant manipulation through kinesthetic and tactile human-robot interaction.

Science.gov (United States)

Kronander, Klas; Billard, Aude

2014-01-01

Robot Learning from Demonstration (RLfD) has been identified as a key element for making robots useful in daily lives. A wide range of techniques has been proposed for deriving a task model from a set of demonstrations of the task. Most previous works use learning to model the kinematics of the task, and for autonomous execution the robot then relies on a stiff position controller. While many tasks can and have been learned this way, there are tasks in which controlling the position alone is insufficient to achieve the goals of the task. These are typically tasks that involve contact or require a specific response to physical perturbations. The question of how to adjust the compliance to suit the need of the task has not yet been fully treated in Robot Learning from Demonstration. In this paper, we address this issue and present interfaces that allow a human teacher to indicate compliance variations by physically interacting with the robot during task execution. We validate our approach in two different experiments on the 7 DoF Barrett WAM and KUKA LWR robot manipulators. Furthermore, we conduct a user study to evaluate the usability of our approach from a non-roboticists perspective.
Robot technologies, autism and designs for learning

DEFF Research Database (Denmark)

Hansbøl, Mikala

2015-01-01

technologies involves several very different educational approaches to supporting young people’s learning and development. The paper discusses how robot technologies as learning resources have been related to the field of autism and education, and argues for a need to further expand the areas of application...... in the future, with a focus on children and young people diagnosed with autism spectrum disorders, their ICT interests and engagement in innovative and creative learning. The paper draws on international research and examples from the author’s own research into education for children and young people diagnosed...... with autism spectrum disorders, drawing on teachers’ and the students’ interests in working with ICT (e.g. robot technology)....
Software for project-based learning of robot motion planning

Science.gov (United States)

Moll, Mark; Bordeaux, Janice; Kavraki, Lydia E.

2013-12-01

Motion planning is a core problem in robotics concerned with finding feasible paths for a given robot. Motion planning algorithms perform a search in the high-dimensional continuous space of robot configurations and exemplify many of the core algorithmic concepts of search algorithms and associated data structures. Motion planning algorithms can be explained in a simplified two-dimensional setting, but this masks many of the subtleties and complexities of the underlying problem. We have developed software for project-based learning of motion planning that enables deep learning. The projects that we have developed allow advanced undergraduate students and graduate students to reflect on the performance of existing textbook algorithms and their own variations on such algorithms. Formative assessment has been conducted at three institutions. The core of the software used for this teaching module is also used within the Robot Operating System, a widely adopted platform by the robotics research community. This allows for transfer of knowledge and skills to robotics research projects involving a large variety robot hardware platforms.
Manifold learning in machine vision and robotics

Science.gov (United States)

Bernstein, Alexander

2017-02-01

Smart algorithms are used in Machine vision and Robotics to organize or extract high-level information from the available data. Nowadays, Machine learning is an essential and ubiquitous tool to automate extraction patterns or regularities from data (images in Machine vision; camera, laser, and sonar sensors data in Robotics) in order to solve various subject-oriented tasks such as understanding and classification of images content, navigation of mobile autonomous robot in uncertain environments, robot manipulation in medical robotics and computer-assisted surgery, and other. Usually such data have high dimensionality, however, due to various dependencies between their components and constraints caused by physical reasons, all "feasible and usable data" occupy only a very small part in high dimensional "observation space" with smaller intrinsic dimensionality. Generally accepted model of such data is manifold model in accordance with which the data lie on or near an unknown manifold (surface) of lower dimensionality embedded in an ambient high dimensional observation space; real-world high-dimensional data obtained from "natural" sources meet, as a rule, this model. The use of Manifold learning technique in Machine vision and Robotics, which discovers a low-dimensional structure of high dimensional data and results in effective algorithms for solving of a large number of various subject-oriented tasks, is the content of the conference plenary speech some topics of which are in the paper.
Robot education peers in a situated primary school study: Personalisation promotes child learning.

Science.gov (United States)

Baxter, Paul; Ashurst, Emily; Read, Robin; Kennedy, James; Belpaeme, Tony

2017-01-01

The benefit of social robots to support child learning in an educational context over an extended period of time is evaluated. Specifically, the effect of personalisation and adaptation of robot social behaviour is assessed. Two autonomous robots were embedded within two matched classrooms of a primary school for a continuous two week period without experimenter supervision to act as learning companions for the children for familiar and novel subjects. Results suggest that while children in both personalised and non-personalised conditions learned, there was increased child learning of a novel subject exhibited when interacting with a robot that personalised its behaviours, with indications that this benefit extended to other class-based performance. Additional evidence was obtained suggesting that there is increased acceptance of the personalised robot peer over a non-personalised version. These results provide the first evidence in support of peer-robot behavioural personalisation having a positive influence on learning when embedded in a learning environment for an extended period of time.
Robot education peers in a situated primary school study: Personalisation promotes child learning.

Directory of Open Access Journals (Sweden)

Paul Baxter

Full Text Available The benefit of social robots to support child learning in an educational context over an extended period of time is evaluated. Specifically, the effect of personalisation and adaptation of robot social behaviour is assessed. Two autonomous robots were embedded within two matched classrooms of a primary school for a continuous two week period without experimenter supervision to act as learning companions for the children for familiar and novel subjects. Results suggest that while children in both personalised and non-personalised conditions learned, there was increased child learning of a novel subject exhibited when interacting with a robot that personalised its behaviours, with indications that this benefit extended to other class-based performance. Additional evidence was obtained suggesting that there is increased acceptance of the personalised robot peer over a non-personalised version. These results provide the first evidence in support of peer-robot behavioural personalisation having a positive influence on learning when embedded in a learning environment for an extended period of time.
Robot education peers in a situated primary school study: Personalisation promotes child learning

Science.gov (United States)

Ashurst, Emily; Read, Robin; Kennedy, James; Belpaeme, Tony

2017-01-01

The benefit of social robots to support child learning in an educational context over an extended period of time is evaluated. Specifically, the effect of personalisation and adaptation of robot social behaviour is assessed. Two autonomous robots were embedded within two matched classrooms of a primary school for a continuous two week period without experimenter supervision to act as learning companions for the children for familiar and novel subjects. Results suggest that while children in both personalised and non-personalised conditions learned, there was increased child learning of a novel subject exhibited when interacting with a robot that personalised its behaviours, with indications that this benefit extended to other class-based performance. Additional evidence was obtained suggesting that there is increased acceptance of the personalised robot peer over a non-personalised version. These results provide the first evidence in support of peer-robot behavioural personalisation having a positive influence on learning when embedded in a learning environment for an extended period of time. PMID:28542648
The Impact of Robot Tutor Nonverbal Social Behavior on Child Learning

Directory of Open Access Journals (Sweden)

James Kennedy

2017-04-01

Full Text Available Several studies have indicated that interacting with social robots in educational contexts may lead to a greater learning than interactions with computers or virtual agents. As such, an increasing amount of social human–robot interaction research is being conducted in the learning domain, particularly with children. However, it is unclear precisely what social behavior a robot should employ in such interactions. Inspiration can be taken from human–human studies; this often leads to an assumption that the more social behavior an agent utilizes, the better the learning outcome will be. We apply a nonverbal behavior metric to a series of studies in which children are taught how to identify prime numbers by a robot with various behavioral manipulations. We find a trend, which generally agrees with the pedagogy literature, but also that overt nonverbal behavior does not account for all learning differences. We discuss the impact of novelty, child expectations, and responses to social cues to further the understanding of the relationship between robot social behavior and learning. We suggest that the combination of nonverbal behavior and social cue congruency is necessary to facilitate learning.
Real-time Stereoscopic 3D for E-Robotics Learning

Directory of Open Access Journals (Sweden)

Richard Y. Chiou

2011-02-01

Full Text Available Following the design and testing of a successful 3-Dimensional surveillance system, this 3D scheme has been implemented into online robotics learning at Drexel University. A real-time application, utilizing robot controllers, programmable logic controllers and sensors, has been developed in the “MET 205 Robotics and Mechatronics” class to provide the students with a better robotic education. The integration of the 3D system allows the students to precisely program the robot and execute functions remotely. Upon the students’ recommendation, polarization has been chosen to be the main platform behind the 3D robotic system. Stereoscopic calculations are carried out for calibration purposes to display the images with the highest possible comfort-level and 3D effect. The calculations are further validated by comparing the results with students’ evaluations. Due to the Internet-based feature, multiple clients have the opportunity to perform the online automation development. In the future, students, in different universities, will be able to cross-control robotic components of different types around the world. With the development of this 3D ERobotics interface, automation resources and robotic learning can be shared and enriched regardless of location.
Human-robot cooperative movement training: Learning a novel sensory motor transformation during walking with robotic assistance-as-needed

Directory of Open Access Journals (Sweden)

Benitez Raul

2007-03-01

Full Text Available Abstract Background A prevailing paradigm of physical rehabilitation following neurologic injury is to "assist-as-needed" in completing desired movements. Several research groups are attempting to automate this principle with robotic movement training devices and patient cooperative algorithms that encourage voluntary participation. These attempts are currently not based on computational models of motor learning. Methods Here we assume that motor recovery from a neurologic injury can be modelled as a process of learning a novel sensory motor transformation, which allows us to study a simplified experimental protocol amenable to mathematical description. Specifically, we use a robotic force field paradigm to impose a virtual impairment on the left leg of unimpaired subjects walking on a treadmill. We then derive an "assist-as-needed" robotic training algorithm to help subjects overcome the virtual impairment and walk normally. The problem is posed as an optimization of performance error and robotic assistance. The optimal robotic movement trainer becomes an error-based controller with a forgetting factor that bounds kinematic errors while systematically reducing its assistance when those errors are small. As humans have a natural range of movement variability, we introduce an error weighting function that causes the robotic trainer to disregard this variability. Results We experimentally validated the controller with ten unimpaired subjects by demonstrating how it helped the subjects learn the novel sensory motor transformation necessary to counteract the virtual impairment, while also preventing them from experiencing large kinematic errors. The addition of the error weighting function allowed the robot assistance to fade to zero even though the subjects' movements were variable. We also show that in order to assist-as-needed, the robot must relax its assistance at a rate faster than that of the learning human. Conclusion The assist
Human-robot cooperative movement training: learning a novel sensory motor transformation during walking with robotic assistance-as-needed.

Science.gov (United States)

Emken, Jeremy L; Benitez, Raul; Reinkensmeyer, David J

2007-03-28

A prevailing paradigm of physical rehabilitation following neurologic injury is to "assist-as-needed" in completing desired movements. Several research groups are attempting to automate this principle with robotic movement training devices and patient cooperative algorithms that encourage voluntary participation. These attempts are currently not based on computational models of motor learning. Here we assume that motor recovery from a neurologic injury can be modelled as a process of learning a novel sensory motor transformation, which allows us to study a simplified experimental protocol amenable to mathematical description. Specifically, we use a robotic force field paradigm to impose a virtual impairment on the left leg of unimpaired subjects walking on a treadmill. We then derive an "assist-as-needed" robotic training algorithm to help subjects overcome the virtual impairment and walk normally. The problem is posed as an optimization of performance error and robotic assistance. The optimal robotic movement trainer becomes an error-based controller with a forgetting factor that bounds kinematic errors while systematically reducing its assistance when those errors are small. As humans have a natural range of movement variability, we introduce an error weighting function that causes the robotic trainer to disregard this variability. We experimentally validated the controller with ten unimpaired subjects by demonstrating how it helped the subjects learn the novel sensory motor transformation necessary to counteract the virtual impairment, while also preventing them from experiencing large kinematic errors. The addition of the error weighting function allowed the robot assistance to fade to zero even though the subjects' movements were variable. We also show that in order to assist-as-needed, the robot must relax its assistance at a rate faster than that of the learning human. The assist-as-needed algorithm proposed here can limit error during the learning of a
Cross-Situational Learning with Bayesian Generative Models for Multimodal Category and Word Learning in Robots

Directory of Open Access Journals (Sweden)

Akira Taniguchi

2017-12-01

Full Text Available In this paper, we propose a Bayesian generative model that can form multiple categories based on each sensory-channel and can associate words with any of the four sensory-channels (action, position, object, and color. This paper focuses on cross-situational learning using the co-occurrence between words and information of sensory-channels in complex situations rather than conventional situations of cross-situational learning. We conducted a learning scenario using a simulator and a real humanoid iCub robot. In the scenario, a human tutor provided a sentence that describes an object of visual attention and an accompanying action to the robot. The scenario was set as follows: the number of words per sensory-channel was three or four, and the number of trials for learning was 20 and 40 for the simulator and 25 and 40 for the real robot. The experimental results showed that the proposed method was able to estimate the multiple categorizations and to learn the relationships between multiple sensory-channels and words accurately. In addition, we conducted an action generation task and an action description task based on word meanings learned in the cross-situational learning scenario. The experimental results showed that the robot could successfully use the word meanings learned by using the proposed method.

A Case-Study for Life-Long Learning and Adaptation in Cooperative Robot Teams

International Nuclear Information System (INIS)

Parker, L.E.

1999-01-01

While considerable progress has been made in recent years toward the development of multi-robot teams, much work remains to be done before these teams are used widely in real-world applications. Two particular needs toward this end are the development of mechanisms that enable robot teams to generate cooperative behaviors on their own, and the development of techniques that allow these teams to autonomously adapt their behavior over time as the environment or the robot team changes. This paper proposes the use of the Cooperative Multi-Robot Observation of Multiple Moving Targets (CMOMMT) application as a rich domain for studying the issues of multi-robot learning and adaptation. After discussing the need for learning and adaptation in multi-robot teams, this paper describes the CMOMMT application and its relevance to multi-robot learning. We discuss the results of the previously- developed, hand-generated algorithm for CMOMMT and the potential for learning that was discovered from the hand-generated approach. We then describe the early work that has been done (by us and others) to generate multi- robot learning techniques for the CMOMMT application, as well as our ongoing research to develop approaches that give performance as good, or better, than the hand-generated approach. The ultimate goal of this research is to develop techniques for multi-robot learning and adaptation in the CMOMMT application domain that will generalize to cooperative robot applications in other domains, thus making the practical use of multi-robot teams in a wide variety of real-world applications much closer to reality
What can we learn from Robots

DEFF Research Database (Denmark)

Clausen, Thea Juhl Roloff

2015-01-01

Robot Technology is going to be a big part of the future, but robotics is also the present. We ask the question, “What can we use this technology for and does it demand a new way to think digital literacy?” One thing we do know is that we must start having focus on the needs the future indicates....... With Robot Technology in a play-and learning perspective it is being a co-producer and being experimental there will be in focus. We must have an understanding of programming language, an understanding of what happens behind the interface and how to use this in an innovative perspective to help shape...
ORGANIZACIJSKO VZDUŠJE V JAVNEM ZAVODU

OpenAIRE

Čeh, Tanja

2011-01-01

Organizacijsko vzdušje je za vsako organizacijo zelo pomembno. Vzdušje v organizaciji je odvisno od zadovoljstva zaposlenih na delovnem mestu, organiziranosti dela, načinu vodenja, medosebnih odnosov, nagradah in željah glede njihovega dela, kariere in izobraževanja. V teoretičnem delu smo bolj podrobno spoznali organizacijsko vzdušje, zgodovino ter dimenzije organizacijskega vzdušja, prav tako smo predstavili organizacijsko kulturo ter razlike med organizacijskim vzdušjem in kulturo ter...
Learning Spatial Object Localization from Vision on a Humanoid Robot

Directory of Open Access Journals (Sweden)

Jürgen Leitner

2012-12-01

Full Text Available We present a combined machine learning and computer vision approach for robots to localize objects. It allows our iCub humanoid to quickly learn to provide accurate 3D position estimates (in the centimetre range of objects seen. Biologically inspired approaches, such as Artificial Neural Networks (ANN and Genetic Programming (GP, are trained to provide these position estimates using the two cameras and the joint encoder readings. No camera calibration or explicit knowledge of the robot's kinematic model is needed. We find that ANN and GP are not just faster and have lower complexity than traditional techniques, but also learn without the need for extensive calibration procedures. In addition, the approach is localizing objects robustly, when placed in the robot's workspace at arbitrary positions, even while the robot is moving its torso, head and eyes.
Gaussian Processes for Data-Efficient Learning in Robotics and Control.

Science.gov (United States)

Deisenroth, Marc Peter; Fox, Dieter; Rasmussen, Carl Edward

2015-02-01

Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.
Evolutionary online behaviour learning and adaptation in real robots.

Science.gov (United States)

Silva, Fernando; Correia, Luís; Christensen, Anders Lyhne

2017-07-01

Online evolution of behavioural control on real robots is an open-ended approach to autonomous learning and adaptation: robots have the potential to automatically learn new tasks and to adapt to changes in environmental conditions, or to failures in sensors and/or actuators. However, studies have so far almost exclusively been carried out in simulation because evolution in real hardware has required several days or weeks to produce capable robots. In this article, we successfully evolve neural network-based controllers in real robotic hardware to solve two single-robot tasks and one collective robotics task. Controllers are evolved either from random solutions or from solutions pre-evolved in simulation. In all cases, capable solutions are found in a timely manner (1 h or less). Results show that more accurate simulations may lead to higher-performing controllers, and that completing the optimization process in real robots is meaningful, even if solutions found in simulation differ from solutions in reality. We furthermore demonstrate for the first time the adaptive capabilities of online evolution in real robotic hardware, including robots able to overcome faults injected in the motors of multiple units simultaneously, and to modify their behaviour in response to changes in the task requirements. We conclude by assessing the contribution of each algorithmic component on the performance of the underlying evolutionary algorithm.
Enhancement of Online Robotics Learning Using Real-Time 3D Visualization Technology

Directory of Open Access Journals (Sweden)

Richard Chiou

2010-06-01

Full Text Available This paper discusses a real-time e-Lab Learning system based on the integration of 3D visualization technology with a remote robotic laboratory. With the emergence and development of the Internet field, online learning is proving to play a significant role in the upcoming era. In an effort to enhance Internet-based learning of robotics and keep up with the rapid progression of technology, a 3- Dimensional scheme of viewing the robotic laboratory has been introduced in addition to the remote controlling of the robots. The uniqueness of the project lies in making this process Internet-based, and remote robot operated and visualized in 3D. This 3D system approach provides the students with a more realistic feel of the 3D robotic laboratory even though they are working remotely. As a result, the 3D visualization technology has been tested as part of a laboratory in the MET 205 Robotics and Mechatronics class and has received positive feedback by most of the students. This type of research has introduced a new level of realism and visual communications to online laboratory learning in a remote classroom.
Nikola Tesla - genije koji je premostio vekove

Directory of Open Access Journals (Sweden)

Vladimir T. Ristić

2006-07-01

Full Text Available Ispisujući prve stranice svoje obimne knjige o životnom putu Nikole Tesle, američki pisac Džon Oí Nil kaže: Tesla je bio mislilac i pronalazač najvišeg reda, onaj što mišljenjem, a ne slučajem, dolazi do otkrića, dok mu eksperiment služi samo za potvrdu njegove teze. On je bio i matematičar, znao je bezbroj formula napamet, tako da se najčešće nije morao služiti priručnicima; osim toga, imao je najsolidnije tehničko obrazovanje, što je, na primer, Edisonu nedostajalo. Nikola Tesla je osnovno školovanje započeo u rodnom Smiljanu, a nastavio i dovršio u Gospiću gde se sa roditeljima, posle smrti starijeg brata, preselio. Gimnaziju je upisao u Karlovcu. Bila je to ugledna škola, a u profesoru fizike Nikola je imao izuzetnog pedagoga, koji je svojim đacima umeo vrlo vešto da dočara čak i ono što je u fizici teško razumljivo.
An architecture for an autonomous learning robot

Science.gov (United States)

Tillotson, Brian

1988-01-01

An autonomous learning device must solve the example bounding problem, i.e., it must divide the continuous universe into discrete examples from which to learn. We describe an architecture which incorporates an example bounder for learning. The architecture is implemented in the GPAL program. An example run with a real mobile robot shows that the program learns and uses new causal, qualitative, and quantitative relationships.
Iterative learning control with sampled-data feedback for robot manipulators

Directory of Open Access Journals (Sweden)

Delchev Kamen

2014-09-01

Full Text Available This paper deals with the improvement of the stability of sampled-data (SD feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more, while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached
Authoring Robot-Assisted Instructional Materials for Improving Learning Performance and Motivation in EFL Classrooms

Science.gov (United States)

Hong, Zeng-Wei; Huang, Yueh-Min; Hsu, Marie; Shen, Wei-Wei

2016-01-01

Anthropomorphized robots are regarded as beneficial tools in education due to their capabilities of improving teaching effectiveness and learning motivation. Therefore, one major trend of research, known as Robot- Assisted Language Learning (RALL), is trying to develop robots to support teaching and learning English as a foreign language (EFL). As…
Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars

Science.gov (United States)

Boucenna, Sofiane; Cohen, David; Meltzoff, Andrew N.; Gaussier, Philippe; Chetouani, Mohamed

2016-02-01

Prior to language, human infants are prolific imitators. Developmental science grounds infant imitation in the neural coding of actions, and highlights the use of imitation for learning from and about people. Here, we used computational modeling and a robot implementation to explore the functional value of action imitation. We report 3 experiments using a mutual imitation task between robots, adults, typically developing children, and children with Autism Spectrum Disorder. We show that a particular learning architecture - specifically one combining artificial neural nets for (i) extraction of visual features, (ii) the robot’s motor internal state, (iii) posture recognition, and (iv) novelty detection - is able to learn from an interactive experience involving mutual imitation. This mutual imitation experience allowed the robot to recognize the interactive agent in a subsequent encounter. These experiments using robots as tools for modeling human cognitive development, based on developmental theory, confirm the promise of developmental robotics. Additionally, findings illustrate how person recognition may emerge through imitative experience, intercorporeal mapping, and statistical learning.
Enhancement of Online Robotics Learning Using Real-Time 3D Visualization Technology

OpenAIRE

Richard Chiou; Yongjin (james) Kwon; Tzu-Liang (bill) Tseng; Robin Kizirian; Yueh-Ting Yang

2010-01-01

This paper discusses a real-time e-Lab Learning system based on the integration of 3D visualization technology with a remote robotic laboratory. With the emergence and development of the Internet field, online learning is proving to play a significant role in the upcoming era. In an effort to enhance Internet-based learning of robotics and keep up with the rapid progression of technology, a 3- Dimensional scheme of viewing the robotic laboratory has been introduced in addition to the remote c...
Training in robotics: The learning curve and contemporary concepts in training.

Science.gov (United States)

Bach, Christian; Miernik, Arkadiusz; Schönthaler, Martin

2014-03-01

To define the learning curve of robot-assisted laparoscopic surgery for prostatectomy (RALP) and upper tract procedures, and show the differences between the classical approach to training and the new concept of parallel learning. This mini-review is based on the results of a Medline search using the keywords 'da Vinci', 'robot-assisted laparoscopic surgery', 'training', 'teaching' and 'learning curve'. For RALP and robot-assisted upper tract surgery, a learning curve of 8-150 procedures is quoted, with most articles proposing that 30-40 cases are needed to carry out the procedure safely. There is no consensus about which endpoints should be measured. In the traditional proctored training model, the surgeon learns the procedure linearly, following the sequential order of the surgical steps. A more recent approach is to specify the relative difficulty of each step and to train the surgeon simultaneously in several steps of equal difficulty. The entire procedure is only performed after all the steps are mastered in a timely manner. Recently, a 'warm-up' before robotic surgery has been shown to be beneficial for successful surgery in the operating room. There is no clear definition of the duration of the effective learning curve for RALP and robotic upper tract surgery. The concept of stepwise, parallel learning has the potential to accelerate the learning process and to make sure that initial cases are not too long. It can also be assumed that a preoperative 'warm up' could help significantly to improve the progress of the trainee.
Embodied Computation: An Active-Learning Approach to Mobile Robotics Education

Science.gov (United States)

Riek, L. D.

2013-01-01

This paper describes a newly designed upper-level undergraduate and graduate course, Autonomous Mobile Robots. The course employs active, cooperative, problem-based learning and is grounded in the fundamental computational problems in mobile robotics defined by Dudek and Jenkin. Students receive a broad survey of robotics through lectures, weekly…
Learning-based identification and iterative learning control of direct-drive robots

NARCIS (Netherlands)

Bukkems, B.H.M.; Kostic, D.; Jager, de A.G.; Steinbuch, M.

2005-01-01

A combination of model-based and Iterative Learning Control is proposed as a method to achieve high-quality motion control of direct-drive robots in repetitive motion tasks. We include both model-based and learning components in the total control law, as their individual properties influence the
STDP-based behavior learning on the TriBot robot

Science.gov (United States)

Arena, P.; De Fiore, S.; Patané, L.; Pollino, M.; Ventura, C.

2009-05-01

This paper describes a correlation-based navigation algorithm, based on an unsupervised learning paradigm for spiking neural networks, called Spike Timing Dependent Plasticity (STDP). This algorithm was implemented on a new bio-inspired hybrid mini-robot called TriBot to learn and increase its behavioral capabilities. In fact correlation based algorithms have been found to explain many basic behaviors in simple animals. The main interesting consequence of STDP is that the system is able to learn high-level sensor features, based on a set of basic reflexes, depending on some low-level sensor inputs. TriBot is composed of 3 modules, the first two being identical and inspired by the Whegs hybrid robot. The peculiar characteristics of the robot consists in the innovative shape of the three-spoke appendages that allow to increase stability of the structure. The last module is composed of two standard legs with 3 degrees of freedom each. Thanks to the cooperation among these modules, TriBot is able to face with irregular terrains overcoming potential deadlock situations, to climb high obstacles compared to its size and to manipulate objects. Robot experiments will be reported to demonstrate the potentiality and the effectiveness of the approach.
Robot Learning from Demonstration: A Task-level Planning Approach

Directory of Open Access Journals (Sweden)

Staffan Ekvall

2008-09-01

Full Text Available In this paper, we deal with the problem of learning by demonstration, task level learning and planning for robotic applications that involve object manipulation. Preprogramming robots for execution of complex domestic tasks such as setting a dinner table is of little use, since the same order of subtasks may not be conceivable in the run time due to the changed state of the world. In our approach, we aim to learn the goal of the task and use a task planner to reach the goal given different initial states of the world. For some tasks, there are underlying constraints that must be fulfille, and knowing just the final goal is not sufficient. We propose two techniques for constraint identification. In the first case, the teacher can directly instruct the system about the underlying constraints. In the second case, the constraints are identified by the robot itself based on multiple observations. The constraints are then considered in the planning phase, allowing the task to be executed without violating any of them. We evaluate our work on a real robot performing pick-and-place tasks.
Robotic Mitral Valve Repair: The Learning Curve.

Science.gov (United States)

Goodman, Avi; Koprivanac, Marijan; Kelava, Marta; Mick, Stephanie L; Gillinov, A Marc; Rajeswaran, Jeevanantham; Brzezinski, Anna; Blackstone, Eugene H; Mihaljevic, Tomislav

Adoption of robotic mitral valve surgery has been slow, likely in part because of its perceived technical complexity and a poorly understood learning curve. We sought to correlate changes in technical performance and outcome with surgeon experience in the "learning curve" part of our series. From 2006 to 2011, two surgeons undertook robotically assisted mitral valve repair in 458 patients (intent-to-treat); 404 procedures were completed entirely robotically (as-treated). Learning curves were constructed by modeling surgical sequence number semiparametrically with flexible penalized spline smoothing best-fit curves. Operative efficiency, reflecting technical performance, improved for (1) operating room time for case 1 to cases 200 (early experience) and 400 (later experience), from 414 to 364 to 321 minutes (12% and 22% decrease, respectively), (2) cardiopulmonary bypass time, from 148 to 102 to 91 minutes (31% and 39% decrease), and (3) myocardial ischemic time, from 119 to 75 to 68 minutes (37% and 43% decrease). Composite postoperative complications, reflecting safety, decreased from 17% to 6% to 2% (63% and 85% decrease). Intensive care unit stay decreased from 32 to 28 to 24 hours (13% and 25% decrease). Postoperative stay fell from 5.2 to 4.5 to 3.8 days (13% and 27% decrease). There were no in-hospital deaths. Predischarge mitral regurgitation of less than 2+, reflecting effectiveness, was achieved in 395 (97.8%), without correlation to experience; return-to-work times did not change substantially with experience. Technical efficiency of robotic mitral valve repair improves with experience and permits its safe and effective conduct.
A neural network-based exploratory learning and motor planning system for co-robots

Directory of Open Access Journals (Sweden)

Byron V Galbraith

2015-07-01

Full Text Available Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or learning by doing, an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.

A neural network-based exploratory learning and motor planning system for co-robots.

Science.gov (United States)

Galbraith, Byron V; Guenther, Frank H; Versace, Massimiliano

2015-01-01

Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or "learning by doing," an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.
Intrinsic interactive reinforcement learning - Using error-related potentials for real world human-robot interaction.

Science.gov (United States)

Kim, Su Kyoung; Kirchner, Elsa Andrea; Stefes, Arne; Kirchner, Frank

2017-12-14

Reinforcement learning (RL) enables robots to learn its optimal behavioral strategy in dynamic environments based on feedback. Explicit human feedback during robot RL is advantageous, since an explicit reward function can be easily adapted. However, it is very demanding and tiresome for a human to continuously and explicitly generate feedback. Therefore, the development of implicit approaches is of high relevance. In this paper, we used an error-related potential (ErrP), an event-related activity in the human electroencephalogram (EEG), as an intrinsically generated implicit feedback (rewards) for RL. Initially we validated our approach with seven subjects in a simulated robot learning scenario. ErrPs were detected online in single trial with a balanced accuracy (bACC) of 91%, which was sufficient to learn to recognize gestures and the correct mapping between human gestures and robot actions in parallel. Finally, we validated our approach in a real robot scenario, in which seven subjects freely chose gestures and the real robot correctly learned the mapping between gestures and actions (ErrP detection (90% bACC)). In this paper, we demonstrated that intrinsically generated EEG-based human feedback in RL can successfully be used to implicitly improve gesture-based robot control during human-robot interaction. We call our approach intrinsic interactive RL.
What pupils can learn from working with robotic direct manipulation environments

NARCIS (Netherlands)

Lou Slangen; Hanno van Keulen; Koeno Gravemeijer

2011-01-01

This study investigates what pupils aged 10-12 can learn from working with robots, assuming that understanding robotics is a sign of technological literacy. We conducted cognitive and conceptual analysis to develop a frame of reference for determining pupils' understanding of robotics. Four
What pupils can learn from working with robotic direct manipulation environments

NARCIS (Netherlands)

Lou Slangen; Hanno van Keulen; Koeno Gravemeijer

2010-01-01

This study investigates what pupils aged 10-12 can learn from working with robots, assuming that understanding robotics is a sign of technological literacy. We conducted cognitive and conceptual analysis to develop a frame of reference for determining pupils' understanding of robotics. Four
Cognitive-Developmental Learning for a Humanoid Robot: A Caregiver's Gift

National Research Council Canada - National Science Library

Arsenio, Artur M

2004-01-01

The goal of this work is to build a cognitive system for the humanoid robot, Cog, that exploits human caregivers as catalysts to perceive and learn about actions, objects, scenes, people, and the robot itself...
A Combination of Machine Learning and Cerebellar Models for the Motor Control and Learning of a Modular Robot

DEFF Research Database (Denmark)

Baira Ojeda, Ismael; Tolu, Silvia; Pacheco, Moises

2017-01-01

We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist, forming a Unit Learning Machine. The LWPR optimizes the input space...... and learns the internal model of a single robot module to command the robot to follow a desired trajectory with its end-effector. The cerebellar microcircuit refines the LWPR output delivering corrective commands. We contrasted distinct cerebellar circuits including analytical models and spiking models...
Bio-robots automatic navigation with graded electric reward stimulation based on Reinforcement Learning.

Science.gov (United States)

Zhang, Chen; Sun, Chao; Gao, Liqiang; Zheng, Nenggan; Chen, Weidong; Zheng, Xiaoxiang

2013-01-01

Bio-robots based on brain computer interface (BCI) suffer from the lack of considering the characteristic of the animals in navigation. This paper proposed a new method for bio-robots' automatic navigation combining the reward generating algorithm base on Reinforcement Learning (RL) with the learning intelligence of animals together. Given the graded electrical reward, the animal e.g. the rat, intends to seek the maximum reward while exploring an unknown environment. Since the rat has excellent spatial recognition, the rat-robot and the RL algorithm can convergent to an optimal route by co-learning. This work has significant inspiration for the practical development of bio-robots' navigation with hybrid intelligence.
SU-G-JeP3-08: Robotic System for Ultrasound Tracking in Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Kuhlemann, I [University of Luebeck, Luebeck (Germany); Graduate School for Computing in Medicine and Life Sciences, University of Luebeck (Germany); Jauer, P; Schweikard, A; Ernst, F [University of Luebeck, Luebeck (Germany)

2016-06-15

Purpose: For safe and accurate real-time tracking of tumors for IGRT using 4D ultrasound, it is necessary to make use of novel, high-end force-sensitive lightweight robots designed for human-machine interaction. Such a robot will be integrated into an existing robotized ultrasound system for non-invasive 4D live tracking, using a newly developed real-time control and communication framework. Methods: The new KUKA LWR iiwa robot is used for robotized ultrasound real-time tumor tracking. Besides more precise probe contact pressure detection, this robot provides an additional 7th link, enhancing the dexterity of the kinematic and the mounted transducer. Several integrated, certified safety features create a safe environment for the patients during treatment. However, to remotely control the robot for the ultrasound application, a real-time control and communication framework has to be developed. Based on a client/server concept, client-side control commands are received and processed by a central server unit and are implemented by a client module running directly on the robot’s controller. Several special functionalities for robotized ultrasound applications are integrated and the robot can now be used for real-time control of the image quality by adjusting the transducer position, and contact pressure. The framework was evaluated looking at overall real-time capability for communication and processing of three different standard commands. Results: Due to inherent, certified safety modules, the new robot ensures a safe environment for patients during tumor tracking. Furthermore, the developed framework shows overall real-time capability with a maximum average latency of 3.6 ms (Minimum 2.5 ms; 5000 trials). Conclusion: The novel KUKA LBR iiwa robot will advance the current robotized ultrasound tracking system with important features. With the developed framework, it is now possible to remotely control this robot and use it for robotized ultrasound tracking
SU-G-JeP3-08: Robotic System for Ultrasound Tracking in Radiation Therapy

International Nuclear Information System (INIS)

Kuhlemann, I; Jauer, P; Schweikard, A; Ernst, F

2016-01-01

Purpose: For safe and accurate real-time tracking of tumors for IGRT using 4D ultrasound, it is necessary to make use of novel, high-end force-sensitive lightweight robots designed for human-machine interaction. Such a robot will be integrated into an existing robotized ultrasound system for non-invasive 4D live tracking, using a newly developed real-time control and communication framework. Methods: The new KUKA LWR iiwa robot is used for robotized ultrasound real-time tumor tracking. Besides more precise probe contact pressure detection, this robot provides an additional 7th link, enhancing the dexterity of the kinematic and the mounted transducer. Several integrated, certified safety features create a safe environment for the patients during treatment. However, to remotely control the robot for the ultrasound application, a real-time control and communication framework has to be developed. Based on a client/server concept, client-side control commands are received and processed by a central server unit and are implemented by a client module running directly on the robot’s controller. Several special functionalities for robotized ultrasound applications are integrated and the robot can now be used for real-time control of the image quality by adjusting the transducer position, and contact pressure. The framework was evaluated looking at overall real-time capability for communication and processing of three different standard commands. Results: Due to inherent, certified safety modules, the new robot ensures a safe environment for patients during tumor tracking. Furthermore, the developed framework shows overall real-time capability with a maximum average latency of 3.6 ms (Minimum 2.5 ms; 5000 trials). Conclusion: The novel KUKA LBR iiwa robot will advance the current robotized ultrasound tracking system with important features. With the developed framework, it is now possible to remotely control this robot and use it for robotized ultrasound tracking
Humanoid Cognitive Robots That Learn by Imitating: Implications for Consciousness Studies

Directory of Open Access Journals (Sweden)

James A. Reggia

2018-01-01

Full Text Available While the concept of a conscious machine is intriguing, producing such a machine remains controversial and challenging. Here, we describe how our work on creating a humanoid cognitive robot that learns to perform tasks via imitation learning relates to this issue. Our discussion is divided into three parts. First, we summarize our previous framework for advancing the understanding of the nature of phenomenal consciousness. This framework is based on identifying computational correlates of consciousness. Second, we describe a cognitive robotic system that we recently developed that learns to perform tasks by imitating human-provided demonstrations. This humanoid robot uses cause–effect reasoning to infer a demonstrator’s intentions in performing a task, rather than just imitating the observed actions verbatim. In particular, its cognitive components center on top-down control of a working memory that retains the explanatory interpretations that the robot constructs during learning. Finally, we describe our ongoing work that is focused on converting our robot’s imitation learning cognitive system into purely neurocomputational form, including both its low-level cognitive neuromotor components, its use of working memory, and its causal reasoning mechanisms. Based on our initial results, we argue that the top-down cognitive control of working memory, and in particular its gating mechanisms, is an important potential computational correlate of consciousness in humanoid robots. We conclude that developing high-level neurocognitive control systems for cognitive robots and using them to search for computational correlates of consciousness provides an important approach to advancing our understanding of consciousness, and that it provides a credible and achievable route to ultimately developing a phenomenally conscious machine.
Learning probabilistic features for robotic navigation using laser sensors.

Directory of Open Access Journals (Sweden)

Fidel Aznar

Full Text Available SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N to O(N(2, where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.
Learning probabilistic features for robotic navigation using laser sensors.

Science.gov (United States)

Aznar, Fidel; Pujol, Francisco A; Pujol, Mar; Rizo, Ramón; Pujol, María-José

2014-01-01

SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N(2)), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.
A line follower robot implementation using Lego's Mindstorms Kit and Q-Learning

Directory of Open Access Journals (Sweden)

Hector-Gabriel Acosta-Mesa

2012-03-01

Full Text Available Un problema común al trabajar con robots móviles es que la fase de programación puede ser un proceso largo, costoso y difícil para los programadores. Los Algoritmos de Aprendizaje por Refuerzo ofrecen uno de los marcos de trabajo más generales en el ámbito de aprendizaje de máquina. Este trabajo presenta un enfoque usando el algoritmo de Q-Learning en un robot Lego para que aprenda "por sí mismo" a seguir una línea negra dibujada en una superficie blanca. El entorno de programación utilizado en este trabajo es Matlab.A common problem working with mobile robots is that programming phase could be a long, expensive and heavy process for programmers. The reinforcement learning algorithms offer one of the most general frameworks in learning subjects. This work presents an approach using the Q-Learning algorithm on a Lego robot in order for it to learn by itself how to follow a blackline drawn down on a white surface, using Matlab [5] as programming environment.
Speeding up the learning of robot kinematics through function decomposition.

Science.gov (United States)

Ruiz de Angulo, Vicente; Torras, Carme

2005-11-01

The main drawback of using neural networks or other example-based learning procedures to approximate the inverse kinematics (IK) of robot arms is the high number of training samples (i.e., robot movements) required to attain an acceptable precision. We propose here a trick, valid for most industrial robots, that greatly reduces the number of movements needed to learn or relearn the IK to a given accuracy. This trick consists in expressing the IK as a composition of learnable functions, each having half the dimensionality of the original mapping. Off-line and on-line training schemes to learn these component functions are also proposed. Experimental results obtained by using nearest neighbors and parameterized self-organizing map, with and without the decomposition, show that the time savings granted by the proposed scheme grow polynomially with the precision required.
Impedance learning for robotic contact tasks using natural actor-critic algorithm.

Science.gov (United States)

Kim, Byungchan; Park, Jooyoung; Park, Shinsuk; Kang, Sungchul

2010-04-01

Compared with their robotic counterparts, humans excel at various tasks by using their ability to adaptively modulate arm impedance parameters. This ability allows us to successfully perform contact tasks even in uncertain environments. This paper considers a learning strategy of motor skill for robotic contact tasks based on a human motor control theory and machine learning schemes. Our robot learning method employs impedance control based on the equilibrium point control theory and reinforcement learning to determine the impedance parameters for contact tasks. A recursive least-square filter-based episodic natural actor-critic algorithm is used to find the optimal impedance parameters. The effectiveness of the proposed method was tested through dynamic simulations of various contact tasks. The simulation results demonstrated that the proposed method optimizes the performance of the contact tasks in uncertain conditions of the environment.
What Pupils Can Learn from Working with Robotic Direct Manipulation Environments

Science.gov (United States)

Slangen, Lou; van Keulen, Hanno; Gravemeijer, Koeno

2011-01-01

This study investigates what pupils aged 10-12 can learn from working with robots, assuming that understanding robotics is a sign of technological literacy. We conducted cognitive and conceptual analysis to develop a frame of reference for determining pupils' understanding of robotics. Four perspectives were distinguished with increasing…
A Contest-Oriented Project for Learning Intelligent Mobile Robots

Science.gov (United States)

Huang, Hsin-Hsiung; Su, Juing-Huei; Lee, Chyi-Shyong

2013-01-01

A contest-oriented project for undergraduate students to learn implementation skills and theories related to intelligent mobile robots is presented in this paper. The project, related to Micromouse, Robotrace (Robotrace is the title of Taiwanese and Japanese robot races), and line-maze contests was developed by the embedded control system research…
Task path planning, scheduling and learning for free-ranging robot systems

Science.gov (United States)

Wakefield, G. Steve

1987-01-01

The development of robotics applications for space operations is often restricted by the limited movement available to guided robots. Free ranging robots can offer greater flexibility than physically guided robots in these applications. Presented here is an object oriented approach to path planning and task scheduling for free-ranging robots that allows the dynamic determination of paths based on the current environment. The system also provides task learning for repetitive jobs. This approach provides a basis for the design of free-ranging robot systems which are adaptable to various environments and tasks.
Optimized Assistive Human-Robot Interaction Using Reinforcement Learning.

Science.gov (United States)

Modares, Hamidreza; Ranatunga, Isura; Lewis, Frank L; Popa, Dan O

2016-03-01

An intelligent human-robot interaction (HRI) system with adjustable robot behavior is presented. The proposed HRI system assists the human operator to perform a given task with minimum workload demands and optimizes the overall human-robot system performance. Motivated by human factor studies, the presented control structure consists of two control loops. First, a robot-specific neuro-adaptive controller is designed in the inner loop to make the unknown nonlinear robot behave like a prescribed robot impedance model as perceived by a human operator. In contrast to existing neural network and adaptive impedance-based control methods, no information of the task performance or the prescribed robot impedance model parameters is required in the inner loop. Then, a task-specific outer-loop controller is designed to find the optimal parameters of the prescribed robot impedance model to adjust the robot's dynamics to the operator skills and minimize the tracking error. The outer loop includes the human operator, the robot, and the task performance details. The problem of finding the optimal parameters of the prescribed robot impedance model is transformed into a linear quadratic regulator (LQR) problem which minimizes the human effort and optimizes the closed-loop behavior of the HRI system for a given task. To obviate the requirement of the knowledge of the human model, integral reinforcement learning is used to solve the given LQR problem. Simulation results on an x - y table and a robot arm, and experimental implementation results on a PR2 robot confirm the suitability of the proposed method.
Learning feedforward controller for a mobile robot vehicle

NARCIS (Netherlands)

Starrenburg, J.G.; Starrenburg, J.G.; van Luenen, W.T.C.; van Luenen, W.T.C.; Oelen, W.; Oelen, W.; van Amerongen, J.

1996-01-01

This paper describes the design and realisation of an on-line learning posetracking controller for a three-wheeled mobile robot vehicle. The controller consists of two components. The first is a constant-gain feedback component, designed on the basis of a second-order model. The second is a learning

The Development of a Robot-Based Learning Companion: A User-Centered Design Approach

Science.gov (United States)

Hsieh, Yi-Zeng; Su, Mu-Chun; Chen, Sherry Y.; Chen, Gow-Dong

2015-01-01

A computer-vision-based method is widely employed to support the development of a variety of applications. In this vein, this study uses a computer-vision-based method to develop a playful learning system, which is a robot-based learning companion named RobotTell. Unlike existing playful learning systems, a user-centered design (UCD) approach is…
Mobile Robot Navigation Based on Q-Learning Technique

Directory of Open Access Journals (Sweden)

Lazhar Khriji

2011-03-01

Full Text Available This paper shows how Q-learning approach can be used in a successful way to deal with the problem of mobile robot navigation. In real situations where a large number of obstacles are involved, normal Q-learning approach would encounter two major problems due to excessively large state space. First, learning the Q-values in tabular form may be infeasible because of the excessive amount of memory needed to store the table. Second, rewards in the state space may be so sparse that with random exploration they will only be discovered extremely slowly. In this paper, we propose a navigation approach for mobile robot, in which the prior knowledge is used within Q-learning. We address the issue of individual behavior design using fuzzy logic. The strategy of behaviors based navigation reduces the complexity of the navigation problem by dividing them in small actions easier for design and implementation. The Q-Learning algorithm is applied to coordinate between these behaviors, which make a great reduction in learning convergence times. Simulation and experimental results confirm the convergence to the desired results in terms of saved time and computational resources.
Learning Robotics in a Science Museum Theatre Play: Investigation of Learning Outcomes, Contexts and Experiences

Science.gov (United States)

Peleg, Ran; Baram-Tsabari, Ayelet

2017-12-01

Theatre is often introduced into science museums to enhance visitor experience. While learning in museums exhibitions received considerable research attention, learning from museum theatre has not. The goal of this exploratory study was to investigate the potential educational role of a science museum theatre play. The study aimed to investigate (1) cognitive learning outcomes of the play, (2) how these outcomes interact with different viewing contexts and (3) experiential learning outcomes through the theatrical experience. The play `Robot and I', addressing principles in robotics, was commissioned by a science museum. Data consisted of 391 questionnaires and interviews with 47 children and 20 parents. Findings indicate that explicit but not implicit learning goals were decoded successfully. There was little synergy between learning outcomes of the play and an exhibition on robotics, demonstrating the effect of two different physical contexts. Interview data revealed that prior knowledge, experience and interest played a major role in children's understanding of the play. Analysis of the theatrical experience showed that despite strong identification with the child protagonist, children often doubted the protagonist's knowledge jeopardizing integration of scientific content. The study extends the empirical knowledge and theoretical thinking on museum theatre to better support claims of its virtues and respond to their criticism.
Unimodal Learning Enhances Crossmodal Learning in Robotic Audio-Visual Tracking

DEFF Research Database (Denmark)

Shaikh, Danish; Bodenhagen, Leon; Manoonpong, Poramate

2017-01-01

Crossmodal sensory integration is a fundamental feature of the brain that aids in forming an coherent and unified representation of observed events in the world. Spatiotemporally correlated sensory stimuli brought about by rich sensorimotor experiences drive the development of crossmodal integrat...... a non-holonomic robotic agent towards a moving audio-visual target. Simulation results demonstrate that unimodal learning enhances crossmodal learning and improves both the overall accuracy and precision of multisensory orientation response....
Unimodal Learning Enhances Crossmodal Learning in Robotic Audio-Visual Tracking

DEFF Research Database (Denmark)

Shaikh, Danish; Bodenhagen, Leon; Manoonpong, Poramate

2018-01-01

Crossmodal sensory integration is a fundamental feature of the brain that aids in forming an coherent and unified representation of observed events in the world. Spatiotemporally correlated sensory stimuli brought about by rich sensorimotor experiences drive the development of crossmodal integrat...... a non-holonomic robotic agent towards a moving audio-visual target. Simulation results demonstrate that unimodal learning enhances crossmodal learning and improves both the overall accuracy and precision of multisensory orientation response....
Self-supervised learning as an enabling technology for future space exploration robots: ISS experiments on monocular distance learning

Science.gov (United States)

van Hecke, Kevin; de Croon, Guido C. H. E.; Hennes, Daniel; Setterfield, Timothy P.; Saenz-Otero, Alvar; Izzo, Dario

2017-11-01

Although machine learning holds an enormous promise for autonomous space robots, it is currently not employed because of the inherent uncertain outcome of learning processes. In this article we investigate a learning mechanism, Self-Supervised Learning (SSL), which is very reliable and hence an important candidate for real-world deployment even on safety-critical systems such as space robots. To demonstrate this reliability, we introduce a novel SSL setup that allows a stereo vision equipped robot to cope with the failure of one of its cameras. The setup learns to estimate average depth using a monocular image, by using the stereo vision depths from the past as trusted ground truth. We present preliminary results from an experiment on the International Space Station (ISS) performed with the MIT/NASA SPHERES VERTIGO satellite. The presented experiments were performed on October 8th, 2015 on board the ISS. The main goals were (1) data gathering, and (2) navigation based on stereo vision. First the astronaut Kimiya Yui moved the satellite around the Japanese Experiment Module to gather stereo vision data for learning. Subsequently, the satellite freely explored the space in the module based on its (trusted) stereo vision system and a pre-programmed exploration behavior, while simultaneously performing the self-supervised learning of monocular depth estimation on board. The two main goals were successfully achieved, representing the first online learning robotic experiments in space. These results lay the groundwork for a follow-up experiment in which the satellite will use the learned single-camera depth estimation for autonomous exploration in the ISS, and are an advancement towards future space robots that continuously improve their navigation capabilities over time, even in harsh and completely unknown space environments.
An iterative learning controller for nonholonomic mobile robots

International Nuclear Information System (INIS)

Oriolo, G.; Panzieri, S.; Ulivi, G.

1998-01-01

The authors present an iterative learning controller that applies to nonholonomic mobile robots, as well as other systems that can be put in chained form. The learning algorithm exploits the fact that chained-form. The learning algorithm exploits the fact that chained-form systems are linear under piecewise-constant inputs. The proposed control scheme requires the execution of a small number of experiments to drive the system to the desired state in finite time, with nice convergence and robustness properties with respect to modeling inaccuracies as well as disturbances. To avoid the necessity of exactly reinitializing the system at each iteration, the basic method is modified so as to obtain a cyclic controller, by which the system is cyclically steered through an arbitrary sequence of states. As a case study, a carlike mobile robot is considered. Both simulation and experimental results are reported to show the performance of the method
Design based action research in the world of robot technology and learning

DEFF Research Database (Denmark)

Majgaard, Gunver

2010-01-01

Why is design based action research method important in the world of robot technology and learning? The article explores how action research and interaction-driven design can be used in development of educational robot technological tools. The actual case is the development of “Fraction Battle......” which is about learning fractions in primary school. The technology is based on robot technology. An outdoor digital playground is taken into to the classroom and then redesigned. The article argues for interaction design takes precedence to technology or goal driven design for development...... of educational tools....
Manifold traversing as a model for learning control of autonomous robots

Science.gov (United States)

Szakaly, Zoltan F.; Schenker, Paul S.

1992-01-01

This paper describes a recipe for the construction of control systems that support complex machines such as multi-limbed/multi-fingered robots. The robot has to execute a task under varying environmental conditions and it has to react reasonably when previously unknown conditions are encountered. Its behavior should be learned and/or trained as opposed to being programmed. The paper describes one possible method for organizing the data that the robot has learned by various means. This framework can accept useful operator input even if it does not fully specify what to do, and can combine knowledge from autonomous, operator assisted and programmed experiences.
Challenges in adapting imitation and reinforcement learning to compliant robots

Directory of Open Access Journals (Sweden)

Calinon Sylvain

2011-12-01

Full Text Available There is an exponential increase of the range of tasks that robots are forecasted to accomplish. (Reprogramming these robots becomes a critical issue for their commercialization and for their applications to real-world scenarios in which users without expertise in robotics wish to adapt the robot to their needs. This paper addresses the problem of designing userfriendly human-robot interfaces to transfer skills in a fast and efﬁcient manner. This paper presents recent work conducted at the Learning and Interaction group at ADVR-IIT, ranging from skill acquisition through kinesthetic teaching to self-reﬁnement strategies initiated from demonstrations. Our group started to explore the use of imitation and exploration strategies that can take advantage of the compliant capabilities of recent robot hardware and control architectures.
Reinforcement function design and bias for efficient learning in mobile robots

International Nuclear Information System (INIS)

Touzet, C.; Santos, J.M.

1998-01-01

The main paradigm in sub-symbolic learning robot domain is the reinforcement learning method. Various techniques have been developed to deal with the memorization/generalization problem, demonstrating the superior ability of artificial neural network implementations. In this paper, the authors address the issue of designing the reinforcement so as to optimize the exploration part of the learning. They also present and summarize works relative to the use of bias intended to achieve the effective synthesis of the desired behavior. Demonstrative experiments involving a self-organizing map implementation of the Q-learning and real mobile robots (Nomad 200 and Khepera) in a task of obstacle avoidance behavior synthesis are described. 3 figs., 5 tabs
Evidence of Self-Directed Learning on a High School Robotics Team

Directory of Open Access Journals (Sweden)

Nathan R. Dolenc

2014-12-01

Full Text Available Self-directed learning is described as an individual taking the initiative to engage in a learning experience while assuming responsibility to follow through to its conclusion. Robotics competitions are examples of informal environments that can facilitate self-directed learning. This study examined how mentor involvement, student behavior, and physical workspace contributed to self-directed learning on one robotics competition team. How did mentors transfer responsibility to students? How did students respond to managing a team? Are the physical attributes of a workspace important? The mentor, student, and workplace factors captured in the research showed mentors wanting students to do the work, students assuming leadership roles, and the limited workspace having a positive effect on student productivity.
Associative learning for a robot intelligence

CERN Document Server

Andreae, John H

1998-01-01

The explanation of brain functioning in terms of the association of ideas has been popular since the 17th century. Recently, however, the process of association has been dismissed as computationally inadequate by prominent cognitive scientists. In this book, a sharper definition of the term "association" is used to revive the process by showing that associative learning can indeed be computationally powerful. Within an appropriate organization, associative learning can be embodied in a robot to realize a human-like intelligence, which sets its own goals, exhibits unique unformalizable behaviou
Beyond adaptive-critic creative learning for intelligent mobile robots

Science.gov (United States)

Liao, Xiaoqun; Cao, Ming; Hall, Ernest L.

2001-10-01

Intelligent industrial and mobile robots may be considered proven technology in structured environments. Teach programming and supervised learning methods permit solutions to a variety of applications. However, we believe that to extend the operation of these machines to more unstructured environments requires a new learning method. Both unsupervised learning and reinforcement learning are potential candidates for these new tasks. The adaptive critic method has been shown to provide useful approximations or even optimal control policies to non-linear systems. The purpose of this paper is to explore the use of new learning methods that goes beyond the adaptive critic method for unstructured environments. The adaptive critic is a form of reinforcement learning. A critic element provides only high level grading corrections to a cognition module that controls the action module. In the proposed system the critic's grades are modeled and forecasted, so that an anticipated set of sub-grades are available to the cognition model. The forecasting grades are interpolated and are available on the time scale needed by the action model. The success of the system is highly dependent on the accuracy of the forecasted grades and adaptability of the action module. Examples from the guidance of a mobile robot are provided to illustrate the method for simple line following and for the more complex navigation and control in an unstructured environment. The theory presented that is beyond the adaptive critic may be called creative theory. Creative theory is a form of learning that models the highest level of human learning - imagination. The application of the creative theory appears to not only be to mobile robots but also to many other forms of human endeavor such as educational learning and business forecasting. Reinforcement learning such as the adaptive critic may be applied to known problems to aid in the discovery of their solutions. The significance of creative theory is that it
Robot Competence Development by Constructive Learning

Science.gov (United States)

Meng, Q.; Lee, M. H.; Hinde, C. J.

This paper presents a constructive learning approach for developing sensor-motor mapping in autonomous systems. The system’s adaptation to environment changes is discussed and three methods are proposed to deal with long term and short term changes. The proposed constructive learning allows autonomous systems to develop network topology and adjust network parameters. The approach is supported by findings from psychology and neuroscience especially during infants cognitive development at early stages. A growing radial basis function network is introduced as a computational substrate for sensory-motor mapping learning. Experiments are conducted on a robot eye/hand coordination testbed and results show the incremental development of sensory-motor mapping and its adaptation to changes such as in tool-use.
Where am I? Contributions to the Localization Problem of Mobile Robots

OpenAIRE

Iser, René

2012-01-01

Das Lokalisierungsproblem mobiler Roboter beschreibt die Aufgabe, deren Pose bezüglich eines gegebenen Weltkoordinatensystems zu bestimmen. Die Fähigkeit zur Selbstlokalisierung wird in vielen Anwendungsbereichen mobiler Roboter benötigt, wie etwa bei dem Materialtransport in der industriellen Fertigung, bei Einsätzen in Katastrophengebieten oder sogar bei der Exploration fremder Planeten. Eine Unterteilung existierender Verfahren zur Lösung des genannten Problems erfolgt je nachdem ob eine L...
Effect of Robotics-Enhanced Inquiry-Based Learning in Elementary Science Education in South Korea

Science.gov (United States)

Park, Jungho

2015-01-01

Much research has been conducted in educational robotics, a new instructional technology, for K-12 education. However, there are arguments on the effect of robotics and limited empirical evidence to investigate the impact of robotics in science learning. Also most robotics studies were carried in an informal educational setting. This study…
Knowledge transfer for learning robot models via local procrustes analysis

CSIR Research Space (South Africa)

Makondo, N

2015-11-01

Full Text Available Learning of robot kinematic and dynamic models from data has attracted much interest recently as an alternative to manually defined models. However, the amount of data required to learn these models becomes large when the number of degrees...
Training and learning robotic surgery, time for a more structured approach: a systematic review

NARCIS (Netherlands)

Schreuder, H. W. R.; Wolswijk, R.; Zweemer, R. P.; Schijven, M. P.; Verheijen, R. H. M.

2012-01-01

Background Robotic assisted laparoscopic surgery is growing rapidly and there is an increasing need for a structured approach to train future robotic surgeons. Objectives To review the literature on training and learning strategies for robotic assisted laparoscopic surgery. Search strategy A
A Scalable Neuro-inspired Robot Controller Integrating a Machine Learning Algorithm and a Spiking Cerebellar-like Network

DEFF Research Database (Denmark)

Baira Ojeda, Ismael; Tolu, Silvia; Lund, Henrik Hautop

2017-01-01

Combining Fable robot, a modular robot, with a neuroinspired controller, we present the proof of principle of a system that can scale to several neurally controlled compliant modules. The motor control and learning of a robot module are carried out by a Unit Learning Machine (ULM) that embeds...... the Locally Weighted Projection Regression algorithm (LWPR) and a spiking cerebellar-like microcircuit. The LWPR guarantees both an optimized representation of the input space and the learning of the dynamic internal model (IM) of the robot. However, the cerebellar-like sub-circuit integrates LWPR input...

Behavioral similarity measurement based on image processing for robots that use imitative learning

Science.gov (United States)

Sterpin B., Dante G.; Martinez S., Fernando; Jacinto G., Edwar

2017-02-01

In the field of the artificial societies, particularly those are based on memetics, imitative behavior is essential for the development of cultural evolution. Applying this concept for robotics, through imitative learning, a robot can acquire behavioral patterns from another robot. Assuming that the learning process must have an instructor and, at least, an apprentice, the fact to obtain a quantitative measurement for their behavioral similarity, would be potentially useful, especially in artificial social systems focused on cultural evolution. In this paper the motor behavior of both kinds of robots, for two simple tasks, is represented by 2D binary images, which are processed in order to measure their behavioral similarity. The results shown here were obtained comparing some similarity measurement methods for binary images.
Robotic Cooperative Learning Promotes Student STEM Interest

Science.gov (United States)

Mosley, Pauline; Ardito, Gerald; Scollins, Lauren

2016-01-01

The principal purpose of this investigation is to study the effect of robotic cooperative learning methodologies on middle school students' critical thinking, and STEM interest. The semi-experimental inquiry consisted of ninety four six-grade students (forty nine students in the experimental group, forty five students in the control group), chosen…
Palankios ugdymui(si) psichologinės ir fizinės aplinkos kūrimas inkliuzinėje klasėje

OpenAIRE

Bartkutė, Aistė

2014-01-01

Bakalauro darbe analizuojamas palankios ugdymui(si) psichologinės ir fizinės aplinkos kūrimas inkliuzinėje klasėje. Tyrime dalyvavo 104 pedagogai, dirbantys Panevėžio miesto ir rajono bendrojo ugdymo mokyklose. Anketinės apklausos metodu tirta pedagogų nuomonė apie palankios ugdymui(si) psichologinės ir fizinės aplinkos kūrimo patirtis ir svarbiausias sąlygas inkliuzinėje klasėje. Analizuojant tyrimo duomenis ieškota ryšio tarp svarbiausių sąlygų, reikalingų palankios ugdymui(si) psicho...
A Combination of Machine Learning and Cerebellar-like Neural Networks for the Motor Control and Motor Learning of the Fable Modular Robot

DEFF Research Database (Denmark)

Baira Ojeda, Ismael; Tolu, Silvia; Pacheco, Moises

2017-01-01

We scaled up a bio-inspired control architecture for the motor control and motor learning of a real modular robot. In our approach, the Locally Weighted Projection Regression algorithm (LWPR) and a cerebellar microcircuit coexist, in the form of a Unit Learning Machine. The LWPR algorithm optimizes...... the input space and learns the internal model of a single robot module to command the robot to follow a desired trajectory with its end-effector. The cerebellar-like microcircuit refines the LWPR output delivering corrective commands. We contrasted distinct cerebellar-like circuits including analytical...
Interactive Rhythm Learning System by Combining Tablet Computers and Robots

Directory of Open Access Journals (Sweden)

Chien-Hsing Chou

2017-03-01

Full Text Available This study proposes a percussion learning device that combines tablet computers and robots. This device comprises two systems: a rhythm teaching system, in which users can compose and practice rhythms by using a tablet computer, and a robot performance system. First, teachers compose the rhythm training contents on the tablet computer. Then, the learners practice these percussion exercises by using the tablet computer and a small drum set. The teaching system provides a new and user-friendly score editing interface for composing a rhythm exercise. It also provides a rhythm rating function to facilitate percussion training for children and improve the stability of rhythmic beating. To encourage children to practice percussion exercises, a robotic performance system is used to interact with the children; this system can perform percussion exercises for students to listen to and then help them practice the exercise. This interaction enhances children’s interest and motivation to learn and practice rhythm exercises. The results of experimental course and field trials reveal that the proposed system not only increases students’ interest and efficiency in learning but also helps them in understanding musical rhythms through interaction and composing simple rhythms.
Autonomous learning in humanoid robotics through mental imagery.

Science.gov (United States)

Di Nuovo, Alessandro G; Marocco, Davide; Di Nuovo, Santo; Cangelosi, Angelo

2013-05-01

In this paper we focus on modeling autonomous learning to improve performance of a humanoid robot through a modular artificial neural networks architecture. A model of a neural controller is presented, which allows a humanoid robot iCub to autonomously improve its sensorimotor skills. This is achieved by endowing the neural controller with a secondary neural system that, by exploiting the sensorimotor skills already acquired by the robot, is able to generate additional imaginary examples that can be used by the controller itself to improve the performance through a simulated mental training. Results and analysis presented in the paper provide evidence of the viability of the approach proposed and help to clarify the rational behind the chosen model and its implementation. Copyright © 2012 Elsevier Ltd. All rights reserved.
Temporal Memory Reinforcement Learning for the Autonomous Micro-mobile Robot Based-behavior

Institute of Scientific and Technical Information of China (English)

Yang Yujun(杨玉君); Cheng Junshi; Chen Jiapin; Li Xiaohai

2004-01-01

This paper presents temporal memory reinforcement learning for the autonomous micro-mobile robot based-behavior. Human being has a memory oblivion process, i.e. the earlier to memorize, the earlier to forget, only the repeated thing can be remembered firmly. Enlightening forms this, and the robot need not memorize all the past states, at the same time economizes the EMS memory space, which is not enough in the MPU of our AMRobot. The proposed algorithm is an extension of the Q-learning, which is an incremental reinforcement learning method. The results of simulation have shown that the algorithm is valid.
A Differentiable Physics Engine for Deep Learning in Robotics

OpenAIRE

Degrave, Jonas; Hermans, Michiel; Dambre, Joni; wyffels, Francis

2016-01-01

One of the most important fields in robotics is the optimization of controllers. Currently, robots are treated as a black box in this optimization process, which is the reason why derivative-free optimization methods such as evolutionary algorithms or reinforcement learning are omnipresent. We propose an implementation of a modern physics engine, which has the ability to differentiate control parameters. This has been implemented on both CPU and GPU. We show how this speeds up the optimizatio...
An appraisal of the learning curve in robotic general surgery.

Science.gov (United States)

Pernar, Luise I M; Robertson, Faith C; Tavakkoli, Ali; Sheu, Eric G; Brooks, David C; Smink, Douglas S

2017-11-01

Robotic-assisted surgery is used with increasing frequency in general surgery for a variety of applications. In spite of this increase in usage, the learning curve is not yet defined. This study reviews the literature on the learning curve in robotic general surgery to inform adopters of the technology. PubMed and EMBASE searches yielded 3690 abstracts published between July 1986 and March 2016. The abstracts were evaluated based on the following inclusion criteria: written in English, reporting original work, focus on general surgery operations, and with explicit statistical methods. Twenty-six full-length articles were included in final analysis. The articles described the learning curves in colorectal (9 articles, 35%), foregut/bariatric (8, 31%), biliary (5, 19%), and solid organ (4, 15%) surgery. Eighteen of 26 (69%) articles report single-surgeon experiences. Time was used as a measure of the learning curve in all studies (100%); outcomes were examined in 10 (38%). In 12 studies (46%), the authors identified three phases of the learning curve. Numbers of cases needed to achieve plateau performance were wide-ranging but overlapping for different kinds of operations: 19-128 cases for colorectal, 8-95 for foregut/bariatric, 20-48 for biliary, and 10-80 for solid organ surgery. Although robotic surgery is increasingly utilized in general surgery, the literature provides few guidelines on the learning curve for adoption. In this heterogeneous sample of reviewed articles, the number of cases needed to achieve plateau performance varies by case type and the learning curve may have multiple phases as surgeons add more complex cases to their case mix with growing experience. Time is the most common determinant for the learning curve. The literature lacks a uniform assessment of outcomes and complications, which would arguably reflect expertise in a more meaningful way than time to perform the operation alone.
Retention of fundamental surgical skills learned in robot-assisted surgery.

Science.gov (United States)

Suh, Irene H; Mukherjee, Mukul; Shah, Bhavin C; Oleynikov, Dmitry; Siu, Ka-Chun

2012-12-01

Evaluation of the learning curve for robotic surgery has shown reduced errors and decreased task completion and training times compared with regular laparoscopic surgery. However, most training evaluations of robotic surgery have only addressed short-term retention after the completion of training. Our goal was to investigate the amount of surgical skills retained after 3 months of training with the da Vinci™ Surgical System. Seven medical students without any surgical experience were recruited. Participants were trained with a 4-day training program of robotic surgical skills and underwent a series of retention tests at 1 day, 1 week, 1 month, and 3 months post-training. Data analysis included time to task completion, speed, distance traveled, and movement curvature by the instrument tip. Performance of the participants was graded using the modified Objective Structured Assessment of Technical Skills (OSATS) for robotic surgery. Participants filled out a survey after each training session by answering a set of questions. Time to task completion and the movement curvature was decreased from pre- to post-training and the performance was retained at all the corresponding retention periods: 1 day, 1 week, 1 month, and 3 months. The modified OSATS showed improvement from pre-test to post-test and this improvement was maintained during all the retention periods. Participants increased in self-confidence and mastery in performing robotic surgical tasks after training. Our novel comprehensive training program improved robot-assisted surgical performance and learning. All trainees retained their fundamental surgical skills for 3 months after receiving the training program.
Arousal regulation and affective adaptation to human responsiveness by a robot that explores and learns a novel environment.

Science.gov (United States)

Hiolle, Antoine; Lewis, Matthew; Cañamero, Lola

2014-01-01

In the context of our work in developmental robotics regarding robot-human caregiver interactions, in this paper we investigate how a "baby" robot that explores and learns novel environments can adapt its affective regulatory behavior of soliciting help from a "caregiver" to the preferences shown by the caregiver in terms of varying responsiveness. We build on two strands of previous work that assessed independently (a) the differences between two "idealized" robot profiles-a "needy" and an "independent" robot-in terms of their use of a caregiver as a means to regulate the "stress" (arousal) produced by the exploration and learning of a novel environment, and (b) the effects on the robot behaviors of two caregiving profiles varying in their responsiveness-"responsive" and "non-responsive"-to the regulatory requests of the robot. Going beyond previous work, in this paper we (a) assess the effects that the varying regulatory behavior of the two robot profiles has on the exploratory and learning patterns of the robots; (b) bring together the two strands previously investigated in isolation and take a step further by endowing the robot with the capability to adapt its regulatory behavior along the "needy" and "independent" axis as a function of the varying responsiveness of the caregiver; and (c) analyze the effects that the varying regulatory behavior has on the exploratory and learning patterns of the adaptive robot.
Adaptive Strategy for Online Gait Learning Evaluated on the Polymorphic Robotic LocoKit

DEFF Research Database (Denmark)

Christensen, David Johan; Larsen, Jørgen Christian; Stoy, Kasper

2012-01-01

This paper presents experiments with a morphologyindependent, life-long strategy for online learning of locomotion gaits, performed on a quadruped robot constructed from the LocoKit modular robot. The learning strategy applies a stochastic optimization algorithm to optimize eight open parameters...... of a central pattern generator based gait implementation. We observe that the strategy converges in roughly ten minutes to gaits of similar or higher velocity than a manually designed gait and that the strategy readapts in the event of failed actuators. In future work we plan to study co-learning...
Cost-effectiveness of Japanese encephalitis (JE) immunization in Bali, Indonesia.

Science.gov (United States)

Liu, Wei; Clemens, John D; Kari, Komang; Xu, Zhi-Yi

2008-08-18

Two hypothetical birth cohorts in Bali, each consisting of 100,000 newborns, one immunized with live, attenuated JE vaccine and the other un-immunized, were modeled for JE risk over 11 years. Cumulative JE incidence before JE vaccine introduction was used to represent JE risk in the unvaccinated cohort. Data on vaccine efficacy, vaccination and treatment costs were taken from published papers and surveys. The potential immunization program averted 54 cases, 5 deaths and saved 1,224 disability adjusted life years (DALYs) at a net cost of USD 700 per JE case averted and USD 31 per DALY saved and thus was highly cost-effective.
Three-dimensional neural net for learning visuomotor coordination of a robot arm.

Science.gov (United States)

Martinetz, T M; Ritter, H J; Schulten, K J

1990-01-01

An extension of T. Kohonen's (1982) self-organizing mapping algorithm together with an error-correction scheme based on the Widrow-Hoff learning rule is applied to develop a learning algorithm for the visuomotor coordination of a simulated robot arm. Learning occurs by a sequence of trial movements without the need for an external teacher. Using input signals from a pair of cameras, the closed robot arm system is able to reduce its positioning error to about 0.3% of the linear dimensions of its work space. This is achieved by choosing the connectivity of a three-dimensional lattice consisting of the units of the neural net.
Hierarchical HMM based learning of navigation primitives for cooperative robotic endovascular catheterization.

Science.gov (United States)

Rafii-Tari, Hedyeh; Liu, Jindong; Payne, Christopher J; Bicknell, Colin; Yang, Guang-Zhong

2014-01-01

Despite increased use of remote-controlled steerable catheter navigation systems for endovascular intervention, most current designs are based on master configurations which tend to alter natural operator tool interactions. This introduces problems to both ergonomics and shared human-robot control. This paper proposes a novel cooperative robotic catheterization system based on learning-from-demonstration. By encoding the higher-level structure of a catheterization task as a sequence of primitive motions, we demonstrate how to achieve prospective learning for complex tasks whilst incorporating subject-specific variations. A hierarchical Hidden Markov Model is used to model each movement primitive as well as their sequential relationship. This model is applied to generation of motion sequences, recognition of operator input, and prediction of future movements for the robot. The framework is validated by comparing catheter tip motions against the manual approach, showing significant improvements in the quality of catheterization. The results motivate the design of collaborative robotic systems that are intuitive to use, while reducing the cognitive workload of the operator.
Design of Intelligent Robot as A Tool for Teaching Media Based on Computer Interactive Learning and Computer Assisted Learning to Improve the Skill of University Student

Science.gov (United States)

Zuhrie, M. S.; Basuki, I.; Asto B, I. G. P.; Anifah, L.

2018-01-01

The focus of the research is the teaching module which incorporates manufacturing, planning mechanical designing, controlling system through microprocessor technology and maneuverability of the robot. Computer interactive and computer-assisted learning is strategies that emphasize the use of computers and learning aids (computer assisted learning) in teaching and learning activity. This research applied the 4-D model research and development. The model is suggested by Thiagarajan, et.al (1974). 4-D Model consists of four stages: Define Stage, Design Stage, Develop Stage, and Disseminate Stage. This research was conducted by applying the research design development with an objective to produce a tool of learning in the form of intelligent robot modules and kit based on Computer Interactive Learning and Computer Assisted Learning. From the data of the Indonesia Robot Contest during the period of 2009-2015, it can be seen that the modules that have been developed confirm the fourth stage of the research methods of development; disseminate method. The modules which have been developed for students guide students to produce Intelligent Robot Tool for Teaching Based on Computer Interactive Learning and Computer Assisted Learning. Results of students’ responses also showed a positive feedback to relate to the module of robotics and computer-based interactive learning.
Memetic Engineering as a Basis for Learning in Robotic Communities

Science.gov (United States)

Truszkowski, Walter F.; Rouff, Christopher; Akhavannik, Mohammad H.

2014-01-01

This paper represents a new contribution to the growing literature on memes. While most memetic thought has been focused on its implications on humans, this paper speculates on the role that memetics can have on robotic communities. Though speculative, the concepts are based on proven advanced multi agent technology work done at NASA - Goddard Space Flight Center and Lockheed Martin. The paper is composed of the following sections : 1) An introductory section which gently leads the reader into the realm of memes. 2) A section on memetic engineering which addresses some of the central issues with robotic learning via memes. 3) A section on related work which very concisely identifies three other areas of memetic applications, i.e., news, psychology, and the study of human behaviors. 4) A section which discusses the proposed approach for realizing memetic behaviors in robots and robotic communities. 5) A section which presents an exploration scenario for a community of robots working on Mars. 6) A final section which discusses future research which will be required to realize a comprehensive science of robotic memetics.
Tema 2: The NAO robot as a Persuasive Educational and Entertainment Robot (PEER – a case study on children’s articulation, categorization and interaction with a social robot for learning

Directory of Open Access Journals (Sweden)

Lykke Brogaard Bertel

2016-01-01

Full Text Available The application of social robots as motivational tools and companions in education is increasingly being explored from a theoretical and practical point of view. In this paper, we examine the social robot NAO as a Persuasive Educational and Entertainment Robot (PEER and present findings from a case study on the use of NAO to support learning environments in Danish primary schools. In the case study we focus on the children’s practice of articulation and embodied interaction with NAO and investigate the role of NAO as a ‘tool’, ‘social actor’ or ‘simulating medium’ in the learning designs. We examine whether this categorization is static or dynamic, i. e. develops and changes over the course of the interaction and explore how this relates to and affects the student’s motivation to engage in the NAO-supported learning activities.
Tema 2: The NAO robot as a Persuasive Educational and Entertainment Robot (PEER – a case study on children’s articulation, categorization and interaction with a social robot for learning

Directory of Open Access Journals (Sweden)

Lykke Brogaard Bertel

2015-12-01

Full Text Available The application of social robots as motivational tools and companions in education is increasingly being explored from a theoretical and practical point of view. In this paper, we examine the social robot NAO as a Persuasive Educational and Entertainment Robot (PEER and present findings from a case study on the use of NAO to support learning environments in Danish primary schools. In the case study we focus on the children’s practice of articulation and embodied interaction with NAO and investigate the role of NAO as a ‘tool’, ‘social actor’ or ‘simulating medium’ in the learning designs. We examine whether this categorization is static or dynamic, i. e. develops and changes over the course of the interaction and explore how this relates to and affects the student’s motivation to engage in the NAO-supported learning activities.
Inquiry learning with a social robot: can you explain that to me?

NARCIS (Netherlands)

Wijnen, Frances Martine; Charisi, Vasiliki; Davison, Daniel Patrick; van der Meij, Jan; Reidsma, Dennis; Evers, Vanessa; Heerink, M.; de Jong, M.

2015-01-01

This paper presents preliminary results of a study which assesses the impact a social robot might have on the verbalization of a child’s internal reasoning and knowledge while working on a learning task. In a comparative experiment we offered children the context of either a social robot or an

Imitation learning of Non-Linear Point-to-Point Robot Motions using Dirichlet Processes

DEFF Research Database (Denmark)

Krüger, Volker; Tikhanoff, Vadim; Natale, Lorenzo

2012-01-01

In this paper we discuss the use of the infinite Gaussian mixture model and Dirichlet processes for learning robot movements from demonstrations. Starting point of this work is an earlier paper where the authors learn a non-linear dynamic robot movement model from a small number of observations....... The model in that work is learned using a classical finite Gaussian mixture model (FGMM) where the Gaussian mixtures are appropriately constrained. The problem with this approach is that one needs to make a good guess for how many mixtures the FGMM should use. In this work, we generalize this approach...... our algorithm on the same data that was used in [5], where the authors use motion capture devices to record the demonstrations. As further validation we test our approach on novel data acquired on our iCub in a different demonstration scenario in which the robot is physically driven by the human...
E-Learning System for Learning Virtual Circuit Making with a Microcontroller and Programming to Control a Robot

Science.gov (United States)

Takemura, Atsushi

2015-01-01

This paper proposes a novel e-Learning system for learning electronic circuit making and programming a microcontroller to control a robot. The proposed e-Learning system comprises a virtual-circuit-making function for the construction of circuits with a versatile, Arduino microcontroller and an educational system that can simulate behaviors of…
Cultural Robotics: The Culture of Robotics and Robotics in Culture

OpenAIRE

Hooman Samani; Elham Saadatian; Natalie Pang; Doros Polydorou; Owen Noel Newton Fernando; Ryohei Nakatsu; Jeffrey Tzu Kwan Valino Koh

2013-01-01

In this paper, we have investigated the concept of “Cultural Robotics” with regard to the evolution of social into cultural robots in the 21st Century. By defining the concept of culture, the potential development of a culture between humans and robots is explored. Based on the cultural values of the robotics developers, and the learning ability of current robots, cultural attributes in this regard are in the process of being formed, which would define the new concept of cultural robotics. Ac...
Designing a Robot Teaching Assistant for Enhancing and Sustaining Learning Motivation

Science.gov (United States)

Hung, I-Chun; Chao, Kuo-Jen; Lee, Ling; Chen, Nian-Shing

2013-01-01

Although many researchers have pointed out that educational robots can stimulate learners' learning motivation, the learning motivation will be hardly sustained and rapidly decreased over time if the amusement and interaction merely come from the new technology itself without incorporating instructional strategies. Many researchers have…
Optimal Search Strategy of Robotic Assembly Based on Neural Vibration Learning

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Lejla Banjanovic-Mehmedovic

2011-01-01

Full Text Available This paper presents implementation of optimal search strategy (OSS in verification of assembly process based on neural vibration learning. The application problem is the complex robot assembly of miniature parts in the example of mating the gears of one multistage planetary speed reducer. Assembly of tube over the planetary gears was noticed as the most difficult problem of overall assembly. The favourable influence of vibration and rotation movement on compensation of tolerance was also observed. With the proposed neural-network-based learning algorithm, it is possible to find extended scope of vibration state parameter. Using optimal search strategy based on minimal distance path between vibration parameter stage sets (amplitude and frequencies of robots gripe vibration and recovery parameter algorithm, we can improve the robot assembly behaviour, that is, allow the fastest possible way of mating. We have verified by using simulation programs that search strategy is suitable for the situation of unexpected events due to uncertainties.
Chimeric classical swine fever (CSF)-Japanese encephalitis (JE) viral replicon as a non-transmissible vaccine candidate against CSF and JE infections.

Science.gov (United States)

Yang, Zhenhua; Wu, Rui; Li, Robert W; Li, Ling; Xiong, Zhongliang; Zhao, Haizhong; Guo, Deyin; Pan, Zishu

2012-04-01

A trans-complemented chimeric CSF-JE virus replicon was constructed using an infectious cDNA clone of the CSF virus (CSFV) Alfort/187 strain. The CSFV E2 gene was deleted, and a fragment containing the region encoding a truncated envelope protein (tE, amino acid 292-402, domain III) of JE virus (JEV) was inserted into the resultant plasmid, pA187delE2, to generate the recombinant cDNA clone pA187delE2/JEV-tE. Porcine kidney 15 (PK15) cells that constitutively express the CSFV E2p7 proteins were then transfected with in vitro-transcribed RNA from pA187delE2/JEV-tE. As a result, the chimeric CSF-JE virus replicon particle (VRP), rv187delE2/JEV-tE, was rescued. In a mouse model, immunization with the chimeric CSF-JE VRP induced strong production of JEV-specific antibody and conferred protection against a lethal JEV challenge. Pigs immunized with CSF-JE VRP displayed strong anti-CSFV and anti-JEV antibody responses and protection against CSFV and JEV challenge infections. Our evidence suggests that E2-complemented CSF-JE VRP not only has potential as a live-attenuated non-transmissible vaccine candidate against CSF and JE but also serves as a potential DIVA (Differentiating Infected from Vaccinated Animals) vaccine for CSF in pigs. Together, our data suggest that the non-transmissible chimeric VRP expressing foreign antigenic proteins may represent a promising strategy for bivalent DIVA vaccine design. Copyright © 2012 Elsevier B.V. All rights reserved.
Personalizing a Service Robot by Learning Human Habits from Behavioral Footprints

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Kun Li

2015-03-01

Full Text Available For a domestic personal robot, personalized services are as important as predesigned tasks, because the robot needs to adjust the home state based on the operator's habits. An operator's habits are composed of cues, behaviors, and rewards. This article introduces behavioral footprints to describe the operator's behaviors in a house, and applies the inverse reinforcement learning technique to extract the operator's habits, represented by a reward function. We implemented the proposed approach with a mobile robot on indoor temperature adjustment, and compared this approach with a baseline method that recorded all the cues and behaviors of the operator. The result shows that the proposed approach allows the robot to reveal the operator's habits accurately and adjust the environment state accordingly.
Emotion in reinforcement learning agents and robots : A survey

NARCIS (Netherlands)

Moerland, T.M.; Broekens, D.J.; Jonker, C.M.

2018-01-01

This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action
Learning-based position control of a closed-kinematic chain robot end-effector

Science.gov (United States)

Nguyen, Charles C.; Zhou, Zhen-Lei

1990-01-01

A trajectory control scheme whose design is based on learning theory, for a six-degree-of-freedom (DOF) robot end-effector built to study robotic assembly of NASA hardwares in space is presented. The control scheme consists of two control systems: the feedback control system and the learning control system. The feedback control system is designed using the concept of linearization about a selected operating point, and the method of pole placement so that the closed-loop linearized system is stabilized. The learning control scheme consisting of PD-type learning controllers, provides additional inputs to improve the end-effector performance after each trial. Experimental studies performed on a 2 DOF end-effector built at CUA, for three tracking cases show that actual trajectories approach desired trajectories as the number of trials increases. The tracking errors are substantially reduced after only five trials.
Learning robotics using Python

CERN Document Server

Joseph, Lentin

2015-01-01

If you are an engineer, a researcher, or a hobbyist, and you are interested in robotics and want to build your own robot, this book is for you. Readers are assumed to be new to robotics but should have experience with Python.
Robot Grasp Learning by Demonstration without Predefined Rules

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César Fernández

2011-12-01

Full Text Available A learning-based approach to autonomous robot grasping is presented. Pattern recognition techniques are used to measure the similarity between a set of previously stored example grasps and all the possible candidate grasps for a new object. Two sets of features are defined in order to characterize grasps: point attributes describe the surroundings of a contact point; point-set attributes describe the relationship between the set of n contact points (assuming an n-fingered robot gripper is used. In the experiments performed, the nearest neighbour classifier outperforms other approaches like multilayer perceptrons, radial basis functions or decision trees, in terms of classification accuracy, while computational load is not excessive for a real time application (a grasp is fully synthesized in 0.2 seconds. The results obtained on a synthetic database show that the proposed system is able to imitate the grasping behaviour of the user (e.g. the system learns to grasp a mug by its handle. All the code has been made available for testing purposes.
Representation Learning of Logic Words by an RNN: From Word Sequences to Robot Actions

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Tatsuro Yamada

2017-12-01

Full Text Available An important characteristic of human language is compositionality. We can efficiently express a wide variety of real-world situations, events, and behaviors by compositionally constructing the meaning of a complex expression from a finite number of elements. Previous studies have analyzed how machine-learning models, particularly neural networks, can learn from experience to represent compositional relationships between language and robot actions with the aim of understanding the symbol grounding structure and achieving intelligent communicative agents. Such studies have mainly dealt with the words (nouns, adjectives, and verbs that directly refer to real-world matters. In addition to these words, the current study deals with logic words, such as “not,” “and,” and “or” simultaneously. These words are not directly referring to the real world, but are logical operators that contribute to the construction of meaning in sentences. In human–robot communication, these words may be used often. The current study builds a recurrent neural network model with long short-term memory units and trains it to learn to translate sentences including logic words into robot actions. We investigate what kind of compositional representations, which mediate sentences and robot actions, emerge as the network's internal states via the learning process. Analysis after learning shows that referential words are merged with visual information and the robot's own current state, and the logical words are represented by the model in accordance with their functions as logical operators. Words such as “true,” “false,” and “not” work as non-linear transformations to encode orthogonal phrases into the same area in a memory cell state space. The word “and,” which required a robot to lift up both its hands, worked as if it was a universal quantifier. The word “or,” which required action generation that looked apparently random, was represented as an
Skill Learning for Intelligent Robot by Perception-Action Integration: A View from Hierarchical Temporal Memory

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Xinzheng Zhang

2017-01-01

Full Text Available Skill learning autonomously through interactions with the environment is a crucial ability for intelligent robot. A perception-action integration or sensorimotor cycle, as an important issue in imitation learning, is a natural mechanism without the complex program process. Recently, neurocomputing model and developmental intelligence method are considered as a new trend for implementing the robot skill learning. In this paper, based on research of the human brain neocortex model, we present a skill learning method by perception-action integration strategy from the perspective of hierarchical temporal memory (HTM theory. The sequential sensor data representing a certain skill from a RGB-D camera are received and then encoded as a sequence of Sparse Distributed Representation (SDR vectors. The sequential SDR vectors are treated as the inputs of the perception-action HTM. The HTM learns sequences of SDRs and makes predictions of what the next input SDR will be. It stores the transitions of the current perceived sensor data and next predicted actions. We evaluated the performance of this proposed framework for learning the shaking hands skill on a humanoid NAO robot. The experimental results manifest that the skill learning method designed in this paper is promising.
Assist-as-needed robotic trainer based on reinforcement learning and its application to dart-throwing.

Science.gov (United States)

Obayashi, Chihiro; Tamei, Tomoya; Shibata, Tomohiro

2014-05-01

This paper proposes a novel robotic trainer for motor skill learning. It is user-adaptive inspired by the assist-as-needed principle well known in the field of physical therapy. Most previous studies in the field of the robotic assistance of motor skill learning have used predetermined desired trajectories, and it has not been examined intensively whether these trajectories were optimal for each user. Furthermore, the guidance hypothesis states that humans tend to rely too much on external assistive feedback, resulting in interference with the internal feedback necessary for motor skill learning. A few studies have proposed a system that adjusts its assistive strength according to the user's performance in order to prevent the user from relying too much on the robotic assistance. There are, however, problems in these studies, in that a physical model of the user's motor system is required, which is inherently difficult to construct. In this paper, we propose a framework for a robotic trainer that is user-adaptive and that neither requires a specific desired trajectory nor a physical model of the user's motor system, and we achieve this using model-free reinforcement learning. We chose dart-throwing as an example motor-learning task as it is one of the simplest throwing tasks, and its performance can easily be and quantitatively measured. Training experiments with novices, aiming at maximizing the score with the darts and minimizing the physical robotic assistance, demonstrate the feasibility and plausibility of the proposed framework. Copyright © 2014 Elsevier Ltd. All rights reserved.
Reaching control of a full-torso, modelled musculoskeletal robot using muscle synergies emergent under reinforcement learning

International Nuclear Information System (INIS)

Diamond, A; Holland, O E

2014-01-01

‘Anthropomimetic’ robots mimic both human morphology and internal structure—skeleton, muscles, compliance and high redundancy—thus presenting a formidable challenge to conventional control. Here we derive a novel controller for this class of robot which learns effective reaching actions through the sustained activation of weighted muscle synergies, an approach which draws upon compelling, recent evidence from animal and human studies, but is almost unexplored to date in the musculoskeletal robot literature. Since the effective synergy patterns for a given robot will be unknown, we derive a reinforcement-learning approach intended to allow their emergence, in particular those patterns aiding linearization of control. Using an extensive physics-based model of the anthropomimetic ECCERobot, we find that effective reaching actions can be learned comprising only two sequential motor co-activation patterns, each controlled by just a single common driving signal. Factor analysis shows the emergent muscle co-activations can be largely reconstructed using weighted combinations of only 13 common fragments. Testing these ‘candidate’ synergies as drivable units, the same controller now learns the reaching task both faster and better. (paper)
A Predictive Study of Learner Attitudes toward Open Learning in a Robotics Class

Science.gov (United States)

Avsec, Stanislav; Rihtarsic, David; Kocijancic, Slavko

2014-01-01

Open learning (OL) strives to transform teaching and learning by applying learning science and emerging technologies to increase student success, improve learning productivity, and lower barriers to access. OL of robotics has a significant growth rate in secondary and/or high schools, but failures exist. Little is known about why many users stop…
Simultaneous development of laparoscopy and robotics provides acceptable perioperative outcomes and shows robotics to have a faster learning curve and to be overall faster in rectal cancer surgery: analysis of novice MIS surgeon learning curves.

Science.gov (United States)

Melich, George; Hong, Young Ki; Kim, Jieun; Hur, Hyuk; Baik, Seung Hyuk; Kim, Nam Kyu; Sender Liberman, A; Min, Byung Soh

2015-03-01

Laparoscopy offers some evidence of benefit compared to open rectal surgery. Robotic rectal surgery is evolving into an accepted approach. The objective was to analyze and compare laparoscopic and robotic rectal surgery learning curves with respect to operative times and perioperative outcomes for a novice minimally invasive colorectal surgeon. One hundred and six laparoscopic and 92 robotic LAR rectal surgery cases were analyzed. All surgeries were performed by a surgeon who was primarily trained in open rectal surgery. Patient characteristics and perioperative outcomes were analyzed. Operative time and CUSUM plots were used for evaluating the learning curve for laparoscopic versus robotic LAR. Laparoscopic versus robotic LAR outcomes feature initial group operative times of 308 (291-325) min versus 397 (373-420) min and last group times of 220 (212-229) min versus 204 (196-211) min-reversed in favor of robotics; major complications of 4.7 versus 6.5 % (NS), resection margin involvement of 2.8 versus 4.4 % (NS), conversion rate of 3.8 versus 1.1 (NS), lymph node harvest of 16.3 versus 17.2 (NS), and estimated blood loss of 231 versus 201 cc (NS). Due to faster learning curves for extracorporeal phase and total mesorectal excision phase, the robotic surgery was observed to be faster than laparoscopic surgery after the initial 41 cases. CUSUM plots demonstrate acceptable perioperative surgical outcomes from the beginning of the study. Initial robotic operative times improved with practice rapidly and eventually became faster than those for laparoscopy. Developing both laparoscopic and robotic skills simultaneously can provide acceptable perioperative outcomes in rectal surgery. It might be suggested that in the current milieu of clashing interests between evolving technology and economic constrains, there might be advantages in embracing both approaches.
Framework for Educational Robotics: A Multiphase Approach to Enhance User Learning in a Competitive Arena

Science.gov (United States)

Lye, Ngit Chan; Wong, Kok Wai; Chiou, Andrew

2013-01-01

Educational robotics involves using robots as an educational tool to provide a long term, and progressive learning activity, to cater to different age group. The current concern is that, using robots in education should not be an instance of a one-off project for the sole purpose of participating in a competitive event. Instead, it should be a…
Representation and Integration: Combining Robot Control, High-Level Planning, and Action Learning

DEFF Research Database (Denmark)

Petrick, Ronald; Kraft, Dirk; Mourao, Kira

We describe an approach to integrated robot control, high-level planning, and action effect learning that attempts to overcome the representational difficulties that exist between these diverse areas. Our approach combines ideas from robot vision, knowledgelevel planning, and connectionist machine......-level action specifications, suitable for planning, from a robot’s interactions with the world. We present a detailed overview of our approach and show how it supports the learning of certain aspects of a high-level lepresentation from low-level world state information....... learning, and focuses on the representational needs of these components.We also make use of a simple representational unit called an instantiated state transition fragment (ISTF) and a related structure called an object-action complex (OAC). The goal of this work is a general approach for inducing high...
Does Prior Laparoscopic and Open Surgery Experience Have Any Impact on Learning Curve in Transition to Robotic Surgery?

Directory of Open Access Journals (Sweden)

Cüneyt Adayener

2016-12-01

Full Text Available It has been 15 years since the Food And Drug Administration approved the Da Vinci® robotic surgery system. Robotic applications are being used extensively in urology, particularly in radical prostatectomy. Like all high-tech products, this system also has a high cost and a steep learning curve, therefore, preventing it from becoming widespread. There are various studies on the effect of open surgery or laparoscopy experience on the learning curve of robotic surgery. Analyzing these interactions well will provide valuable information on making the training period of robotic system more efficient.

Learning curve for robotic-assisted surgery for rectal cancer: use of the cumulative sum method.

Science.gov (United States)

Yamaguchi, Tomohiro; Kinugasa, Yusuke; Shiomi, Akio; Sato, Sumito; Yamakawa, Yushi; Kagawa, Hiroyasu; Tomioka, Hiroyuki; Mori, Keita

2015-07-01

Few data are available to assess the learning curve for robotic-assisted surgery for rectal cancer. The aim of the present study was to evaluate the learning curve for robotic-assisted surgery for rectal cancer by a surgeon at a single institute. From December 2011 to August 2013, a total of 80 consecutive patients who underwent robotic-assisted surgery for rectal cancer performed by the same surgeon were included in this study. The learning curve was analyzed using the cumulative sum method. This method was used for all 80 cases, taking into account operative time. Operative procedures included anterior resections in 6 patients, low anterior resections in 46 patients, intersphincteric resections in 22 patients, and abdominoperineal resections in 6 patients. Lateral lymph node dissection was performed in 28 patients. Median operative time was 280 min (range 135-683 min), and median blood loss was 17 mL (range 0-690 mL). No postoperative complications of Clavien-Dindo classification Grade III or IV were encountered. We arranged operative times and calculated cumulative sum values, allowing differentiation of three phases: phase I, Cases 1-25; phase II, Cases 26-50; and phase III, Cases 51-80. Our data suggested three phases of the learning curve in robotic-assisted surgery for rectal cancer. The first 25 cases formed the learning phase.
Cultural Robotics: The Culture of Robotics and Robotics in Culture

Directory of Open Access Journals (Sweden)

Hooman Samani

2013-12-01

Full Text Available In this paper, we have investigated the concept of “Cultural Robotics” with regard to the evolution of social into cultural robots in the 21st Century. By defining the concept of culture, the potential development of a culture between humans and robots is explored. Based on the cultural values of the robotics developers, and the learning ability of current robots, cultural attributes in this regard are in the process of being formed, which would define the new concept of cultural robotics. According to the importance of the embodiment of robots in the sense of presence, the influence of robots in communication culture is anticipated. The sustainability of robotics culture based on diversity for cultural communities for various acceptance modalities is explored in order to anticipate the creation of different attributes of culture between robots and humans in the future.
Fuzzy control in robot-soccer, evolutionary learning in the first layer of control

Directory of Open Access Journals (Sweden)

Peter J Thomas

2003-02-01

Full Text Available In this paper an evolutionary algorithm is developed to learn a fuzzy knowledge base for the control of a soccer playing micro-robot from any configuration belonging to a grid of initial configurations to hit the ball along the ball to goal line of sight. The knowledge base uses relative co-ordinate system including left and right wheel velocities of the robot. Final path positions allow forward and reverse facing robot to ball and include its physical dimensions.
Vision-based Navigation and Reinforcement Learning Path Finding for Social Robots

OpenAIRE

Pérez Sala, Xavier

2010-01-01

We propose a robust system for automatic Robot Navigation in uncontrolled en- vironments. The system is composed by three main modules: the Arti cial Vision module, the Reinforcement Learning module, and the behavior control module. The aim of the system is to allow a robot to automatically nd a path that arrives to a pre xed goal. Turn and straight movements in uncontrolled environments are automatically estimated and controlled using the proposed modules. The Arti cial Vi...
Design-Oriented Enhanced Robotics Curriculum

Science.gov (United States)

Yilmaz, M.; Ozcelik, S.; Yilmazer, N.; Nekovei, R.

2013-01-01

This paper presents an innovative two-course, laboratory-based, and design-oriented robotics educational model. The robotics curriculum exposed senior-level undergraduate students to major robotics concepts, and enhanced the student learning experience in hybrid learning environments by incorporating the IEEE Region-5 annual robotics competition…
Toward cognitive robotics

Science.gov (United States)

Laird, John E.

2009-05-01

Our long-term goal is to develop autonomous robotic systems that have the cognitive abilities of humans, including communication, coordination, adapting to novel situations, and learning through experience. Our approach rests on the recent integration of the Soar cognitive architecture with both virtual and physical robotic systems. Soar has been used to develop a wide variety of knowledge-rich agents for complex virtual environments, including distributed training environments and interactive computer games. For development and testing in robotic virtual environments, Soar interfaces to a variety of robotic simulators and a simple mobile robot. We have recently made significant extensions to Soar that add new memories and new non-symbolic reasoning to Soar's original symbolic processing, which should significantly improve Soar abilities for control of robots. These extensions include episodic memory, semantic memory, reinforcement learning, and mental imagery. Episodic memory and semantic memory support the learning and recalling of prior events and situations as well as facts about the world. Reinforcement learning provides the ability of the system to tune its procedural knowledge - knowledge about how to do things. Mental imagery supports the use of diagrammatic and visual representations that are critical to support spatial reasoning. We speculate on the future of unmanned systems and the need for cognitive robotics to support dynamic instruction and taskability.
Interactive language learning by robots: the transition from babbling to word forms.

Science.gov (United States)

Lyon, Caroline; Nehaniv, Chrystopher L; Saunders, Joe

2012-01-01

The advent of humanoid robots has enabled a new approach to investigating the acquisition of language, and we report on the development of robots able to acquire rudimentary linguistic skills. Our work focuses on early stages analogous to some characteristics of a human child of about 6 to 14 months, the transition from babbling to first word forms. We investigate one mechanism among many that may contribute to this process, a key factor being the sensitivity of learners to the statistical distribution of linguistic elements. As well as being necessary for learning word meanings, the acquisition of anchor word forms facilitates the segmentation of an acoustic stream through other mechanisms. In our experiments some salient one-syllable word forms are learnt by a humanoid robot in real-time interactions with naive participants. Words emerge from random syllabic babble through a learning process based on a dialogue between the robot and the human participant, whose speech is perceived by the robot as a stream of phonemes. Numerous ways of representing the speech as syllabic segments are possible. Furthermore, the pronunciation of many words in spontaneous speech is variable. However, in line with research elsewhere, we observe that salient content words are more likely than function words to have consistent canonical representations; thus their relative frequency increases, as does their influence on the learner. Variable pronunciation may contribute to early word form acquisition. The importance of contingent interaction in real-time between teacher and learner is reflected by a reinforcement process, with variable success. The examination of individual cases may be more informative than group results. Nevertheless, word forms are usually produced by the robot after a few minutes of dialogue, employing a simple, real-time, frequency dependent mechanism. This work shows the potential of human-robot interaction systems in studies of the dynamics of early language
Locomotion training of legged robots using hybrid machine learning techniques

Science.gov (United States)

Simon, William E.; Doerschuk, Peggy I.; Zhang, Wen-Ran; Li, Andrew L.

1995-01-01

In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible
Diffusion of robotics into clinical practice in the United States: process, patient safety, learning curves, and the public health.

Science.gov (United States)

Mirheydar, Hossein S; Parsons, J Kellogg

2013-06-01

Robotic technology disseminated into urological practice without robust comparative effectiveness data. To review the diffusion of robotic surgery into urological practice. We performed a comprehensive literature review focusing on diffusion patterns, patient safety, learning curves, and comparative costs for robotic radical prostatectomy, partial nephrectomy, and radical cystectomy. Robotic urologic surgery diffused in patterns typical of novel technology spreading among practicing surgeons. Robust evidence-based data comparing outcomes of robotic to open surgery were sparse. Although initial Level 3 evidence for robotic prostatectomy observed complication outcomes similar to open prostatectomy, subsequent population-based Level 2 evidence noted an increased prevalence of adverse patient safety events and genitourinary complications among robotic patients during the early years of diffusion. Level 2 evidence indicated comparable to improved patient safety outcomes for robotic compared to open partial nephrectomy and cystectomy. Learning curve recommendations for robotic urologic surgery have drawn exclusively on Level 4 evidence and subjective, non-validated metrics. The minimum number of cases required to achieve competency for robotic prostatectomy has increased to unrealistically high levels. Most comparative cost-analyses have demonstrated that robotic surgery is significantly more expensive than open or laparoscopic surgery. Evidence-based data are limited but suggest an increased prevalence of adverse patient safety events for robotic prostatectomy early in the national diffusion period. Learning curves for robotic urologic surgery are subjective and based on non-validated metrics. The urological community should develop rigorous, evidence-based processes by which future technological innovations may diffuse in an organized and safe manner.
Case Study Analyses of the Impact of Flipped Learning in Teaching Programming Robots

Directory of Open Access Journals (Sweden)

Majlinda Fetaji

2016-11-01

Full Text Available The focus of the research study was to investigate and find out the benefits of the flipped learning pedagogy on the student learning in teaching programming Robotics classes. Also, the assessment of whether it has any advantages over the traditional teaching methods in computer sciences. Assessment of learners on their attitudes, motivation, and effectiveness when using flipped classroom compared with traditional classroom has been realized. The research questions investigated are: “What kind of problems can we face when we have robotics classes in the traditional methods?”, “If we applied flipped learning method, can we solve these problems?”. In order to analyze all this, a case study experiment was realized and insights as well as recommendations are presented.
Les je gigognes du roman célinien

Directory of Open Access Journals (Sweden)

Gregor Perko

2008-12-01

Full Text Available Le présent article se penchera sur des aspects narratologiques des trois derniers romans de l’écrivain français Louis-Ferdinand Céline (1894–1961, d’un château l’autre (1957, nord (1960 et rigodon (publication posthume en 1964. Les romans, que la tradition critique solidement établie réunit en trilogie allemande,1 présentent l’aboutissement des recherches poétiques de l’écrivain tant au niveau du style qu’au niveau des techniques narratives. L’analyse qui s’appuiera pour l’essentiel sur le modèle narratologique de Gérard Genette (Genette 1972, 1983 se centrera sur différentes valeurs du je célinien : – je comme instance(s narrative(s, – je comme foyer(s de perception, – je comme personnage(s romanesque(s.
Mikroprocesorem řízená nabíječka baterií

OpenAIRE

Džama, Igor

2016-01-01

Tato diplomová práce se zabývá návrhem mikroprocesorem řízené nabíječky baterií. Práce obsahuje obvodové schéma zapojení výsledné nabíječky. V této práci je vytvořen funkční software pro řízení této nabíječky. Nabíječka je pak vyrobena. This diploma thesis deals with design of microprocessor controlled battery charger. Thesis contains circuit diagram of final charger. In this thesist there is created functioning software for control of this battery charger. Charger is than manufactured. ...
Alignment Condition-Based Robust Adaptive Iterative Learning Control of Uncertain Robot System

Directory of Open Access Journals (Sweden)

Guofeng Tong

2014-04-01

Full Text Available This paper proposes an adaptive iterative learning control strategy integrated with saturation-based robust control for uncertain robot system in presence of modelling uncertainties, unknown parameter, and external disturbance under alignment condition. An important merit is that it achieves adaptive switching of gain matrix both in conventional PD-type feedforward control and robust adaptive control in the iteration domain simultaneously. The analysis of convergence of proposed control law is based on Lyapunov's direct method under alignment initial condition. Simulation results demonstrate the faster learning rate and better robust performance with proposed algorithm by comparing with other existing robust controllers. The actual experiment on three-DOF robot manipulator shows its better practical effectiveness.
A Predictive Study of Learner Attitudes Toward Open Learning in a Robotics Class

Science.gov (United States)

Avsec, Stanislav; Rihtarsic, David; Kocijancic, Slavko

2014-10-01

Open learning (OL) strives to transform teaching and learning by applying learning science and emerging technologies to increase student success, improve learning productivity, and lower barriers to access. OL of robotics has a significant growth rate in secondary and/or high schools, but failures exist. Little is known about why many users stop their OL after their initial experience. Previous research done under different task environments has suggested a variety of factors affecting user satisfaction with different types of OL. In this study, we tested a regression model for student satisfaction involving students' attitudes toward OL usage. A survey was conducted to investigate the critical factors affecting students' achievements and satisfaction in OL of robotics with use of own developed direct manipulation learning environment as learning context. A multiple regression analyses were carried out to investigate how different facets of students' expectations and experiences are related to perceived learning achievements and course satisfaction. Descriptive statistics and analysis of variance was performed to determine the effect of predictor variables to student satisfaction. The results demonstrate that students have significantly positive perceptions toward using OL of robotics as a learning-assisted tool. Furthermore, behavioral intention to use OL is influenced by perceived usefulness and self-efficacy. The following five major categories of satisfaction factors with OL course were revealed during analysis of the studies (effect sizes in parentheses): organization (0.69); implementation (0.61); professional content (0.53); interaction (0.43); self-efficacy (0.14). All these effect sizes were judged to be significant and large. The results also showed that learner-mentor/instructor interaction, learner-professional content interaction, and online and offline self-efficacy were good predictors of student satisfaction and course quality. Peer interactions and
Research on Open-Closed-Loop Iterative Learning Control with Variable Forgetting Factor of Mobile Robots

Directory of Open Access Journals (Sweden)

Hongbin Wang

2016-01-01

Full Text Available We propose an iterative learning control algorithm (ILC that is developed using a variable forgetting factor to control a mobile robot. The proposed algorithm can be categorized as an open-closed-loop iterative learning control, which produces control instructions by using both previous and current data. However, introducing a variable forgetting factor can weaken the former control output and its variance in the control law while strengthening the robustness of the iterative learning control. If it is applied to the mobile robot, this will reduce position errors in robot trajectory tracking control effectively. In this work, we show that the proposed algorithm guarantees tracking error bound convergence to a small neighborhood of the origin under the condition of state disturbances, output measurement noises, and fluctuation of system dynamics. By using simulation, we demonstrate that the controller is effective in realizing the prefect tracking.
Robot initiative in a team learning task increases the rhythm of interaction but not the perceived engagement

Science.gov (United States)

Ivaldi, Serena; Anzalone, Salvatore M.; Rousseau, Woody; Sigaud, Olivier; Chetouani, Mohamed

2014-01-01

We hypothesize that the initiative of a robot during a collaborative task with a human can influence the pace of interaction, the human response to attention cues, and the perceived engagement. We propose an object learning experiment where the human interacts in a natural way with the humanoid iCub. Through a two-phases scenario, the human teaches the robot about the properties of some objects. We compare the effect of the initiator of the task in the teaching phase (human or robot) on the rhythm of the interaction in the verification phase. We measure the reaction time of the human gaze when responding to attention utterances of the robot. Our experiments show that when the robot is the initiator of the learning task, the pace of interaction is higher and the reaction to attention cues faster. Subjective evaluations suggest that the initiating role of the robot, however, does not affect the perceived engagement. Moreover, subjective and third-person evaluations of the interaction task suggest that the attentive mechanism we implemented in the humanoid robot iCub is able to arouse engagement and make the robot's behavior readable. PMID:24596554
Human-robot skills transfer interfaces for a flexible surgical robot.

Science.gov (United States)

Calinon, Sylvain; Bruno, Danilo; Malekzadeh, Milad S; Nanayakkara, Thrishantha; Caldwell, Darwin G

2014-09-01

In minimally invasive surgery, tools go through narrow openings and manipulate soft organs to perform surgical tasks. There are limitations in current robot-assisted surgical systems due to the rigidity of robot tools. The aim of the STIFF-FLOP European project is to develop a soft robotic arm to perform surgical tasks. The flexibility of the robot allows the surgeon to move within organs to reach remote areas inside the body and perform challenging procedures in laparoscopy. This article addresses the problem of designing learning interfaces enabling the transfer of skills from human demonstration. Robot programming by demonstration encompasses a wide range of learning strategies, from simple mimicking of the demonstrator's actions to the higher level imitation of the underlying intent extracted from the demonstrations. By focusing on this last form, we study the problem of extracting an objective function explaining the demonstrations from an over-specified set of candidate reward functions, and using this information for self-refinement of the skill. In contrast to inverse reinforcement learning strategies that attempt to explain the observations with reward functions defined for the entire task (or a set of pre-defined reward profiles active for different parts of the task), the proposed approach is based on context-dependent reward-weighted learning, where the robot can learn the relevance of candidate objective functions with respect to the current phase of the task or encountered situation. The robot then exploits this information for skills refinement in the policy parameters space. The proposed approach is tested in simulation with a cutting task performed by the STIFF-FLOP flexible robot, using kinesthetic demonstrations from a Barrett WAM manipulator. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Action understanding and imitation learning in a robot-human task

NARCIS (Netherlands)

Erlhagen, W.; Mukovskiy, A.; Bicho, E.; Panin, G.; Kiss, C.; Knoll, A.; Schie, H.T. van; Bekkering, H.

2005-01-01

We report results of an interdisciplinary project which aims at endowing a real robot system with the capacity for learning by goal-directed imitation. The control architecture is biologically inspired as it reflects recent experimental findings in action observation/execution studies. We test its
Understanding Human Hand Gestures for Learning Robot Pick-and-Place Tasks

Directory of Open Access Journals (Sweden)

Hsien-I Lin

2015-05-01

Full Text Available Programming robots by human demonstration is an intuitive approach, especially by gestures. Because robot pick-and-place tasks are widely used in industrial factories, this paper proposes a framework to learn robot pick-and-place tasks by understanding human hand gestures. The proposed framework is composed of the module of gesture recognition and the module of robot behaviour control. For the module of gesture recognition, transport empty (TE, transport loaded (TL, grasp (G, and release (RL from Gilbreth's therbligs are the hand gestures to be recognized. A convolution neural network (CNN is adopted to recognize these gestures from a camera image. To achieve the robust performance, the skin model by a Gaussian mixture model (GMM is used to filter out non-skin colours of an image, and the calibration of position and orientation is applied to obtain the neutral hand pose before the training and testing of the CNN. For the module of robot behaviour control, the corresponding robot motion primitives to TE, TL, G, and RL, respectively, are implemented in the robot. To manage the primitives in the robot system, a behaviour-based programming platform based on the Extensible Agent Behavior Specification Language (XABSL is adopted. Because the XABSL provides the flexibility and re-usability of the robot primitives, the hand motion sequence from the module of gesture recognition can be easily used in the XABSL programming platform to implement the robot pick-and-place tasks. The experimental evaluation of seven subjects performing seven hand gestures showed that the average recognition rate was 95.96%. Moreover, by the XABSL programming platform, the experiment showed the cube-stacking task was easily programmed by human demonstration.
Interaction learning for dynamic movement primitives used in cooperative robotic tasks

DEFF Research Database (Denmark)

Kulvicius, Tomas; Biehl, Martin; Aein, Mohamad Javad

2013-01-01

Abstract Since several years dynamic movement primitives (DMPs) are more and more getting into the center of interest for flexible movement control in robotics. In this study we introduce sensory feedback together with a predictive learning mechanism which allows tightly coupled dual-agent systems...... to learn an adaptive, sensor-driven interaction based on DMPs. The coupled conventional (no-sensors, no learning) DMP-system automatically equilibrates and can still be solved analytically allowing us to derive conditions for stability. When adding adaptive sensor control we can show that both agents learn...

Pragmatic Frames for Teaching and Learning in Human-Robot Interaction: Review and Challenges.

Science.gov (United States)

Vollmer, Anna-Lisa; Wrede, Britta; Rohlfing, Katharina J; Oudeyer, Pierre-Yves

2016-01-01

One of the big challenges in robotics today is to learn from human users that are inexperienced in interacting with robots but yet are often used to teach skills flexibly to other humans and to children in particular. A potential route toward natural and efficient learning and teaching in Human-Robot Interaction (HRI) is to leverage the social competences of humans and the underlying interactional mechanisms. In this perspective, this article discusses the importance of pragmatic frames as flexible interaction protocols that provide important contextual cues to enable learners to infer new action or language skills and teachers to convey these cues. After defining and discussing the concept of pragmatic frames, grounded in decades of research in developmental psychology, we study a selection of HRI work in the literature which has focused on learning-teaching interaction and analyze the interactional and learning mechanisms that were used in the light of pragmatic frames. This allows us to show that many of the works have already used in practice, but not always explicitly, basic elements of the pragmatic frames machinery. However, we also show that pragmatic frames have so far been used in a very restricted way as compared to how they are used in human-human interaction and argue that this has been an obstacle preventing robust natural multi-task learning and teaching in HRI. In particular, we explain that two central features of human pragmatic frames, mostly absent of existing HRI studies, are that (1) social peers use rich repertoires of frames, potentially combined together, to convey and infer multiple kinds of cues; (2) new frames can be learnt continually, building on existing ones, and guiding the interaction toward higher levels of complexity and expressivity. To conclude, we give an outlook on the future research direction describing the relevant key challenges that need to be solved for leveraging pragmatic frames for robot learning and teaching.
Autonomous military robotics

CERN Document Server

Nath, Vishnu

2014-01-01

This SpringerBrief reveals the latest techniques in computer vision and machine learning on robots that are designed as accurate and efficient military snipers. Militaries around the world are investigating this technology to simplify the time, cost and safety measures necessary for training human snipers. These robots are developed by combining crucial aspects of computer science research areas including image processing, robotic kinematics and learning algorithms. The authors explain how a new humanoid robot, the iCub, uses high-speed cameras and computer vision algorithms to track the objec
Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.

Science.gov (United States)

Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline; Sanchez, Justin C

2012-01-01

Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.
Anticipatory Driving for a Robot-Car Based on Supervised Learning

DEFF Research Database (Denmark)

Markelic, I.; Kulvicius, Tomas; Tamosiunaite, M.

2009-01-01

Using look ahead information and plan making improves hu- man driving. We therefore propose that also autonomously driving systems should dispose over such abilities. We adapt a machine learning approach, where the system, a car-like robot, is trained by an experienced driver by correlating visual...
Multiagent Reinforcement Learning with Regret Matching for Robot Soccer

Directory of Open Access Journals (Sweden)

Qiang Liu

2013-01-01

Full Text Available This paper proposes a novel multiagent reinforcement learning (MARL algorithm Nash- learning with regret matching, in which regret matching is used to speed up the well-known MARL algorithm Nash- learning. It is critical that choosing a suitable strategy for action selection to harmonize the relation between exploration and exploitation to enhance the ability of online learning for Nash- learning. In Markov Game the joint action of agents adopting regret matching algorithm can converge to a group of points of no-regret that can be viewed as coarse correlated equilibrium which includes Nash equilibrium in essence. It is can be inferred that regret matching can guide exploration of the state-action space so that the rate of convergence of Nash- learning algorithm can be increased. Simulation results on robot soccer validate that compared to original Nash- learning algorithm, the use of regret matching during the learning phase of Nash- learning has excellent ability of online learning and results in significant performance in terms of scores, average reward and policy convergence.
Put Your Robot In, Put Your Robot Out: Sequencing through Programming Robots in Early Childhood

Science.gov (United States)

Kazakoff, Elizabeth R.; Bers, Marina Umaschi

2014-01-01

This article examines the impact of programming robots on sequencing ability in early childhood. Thirty-four children (ages 4.5-6.5 years) participated in computer programming activities with a developmentally appropriate tool, CHERP, specifically designed to program a robot's behaviors. The children learned to build and program robots over three…
A learning-based semi-autonomous controller for robotic exploration of unknown disaster scenes while searching for victims.

Science.gov (United States)

Doroodgar, Barzin; Liu, Yugang; Nejat, Goldie

2014-12-01

Semi-autonomous control schemes can address the limitations of both teleoperation and fully autonomous robotic control of rescue robots in disaster environments by allowing a human operator to cooperate and share such tasks with a rescue robot as navigation, exploration, and victim identification. In this paper, we present a unique hierarchical reinforcement learning-based semi-autonomous control architecture for rescue robots operating in cluttered and unknown urban search and rescue (USAR) environments. The aim of the controller is to enable a rescue robot to continuously learn from its own experiences in an environment in order to improve its overall performance in exploration of unknown disaster scenes. A direction-based exploration technique is integrated in the controller to expand the search area of the robot via the classification of regions and the rubble piles within these regions. Both simulations and physical experiments in USAR-like environments verify the robustness of the proposed HRL-based semi-autonomous controller to unknown cluttered scenes with different sizes and varying types of configurations.
A non-linear manifold alignment approach to robot learning from demonstrations

CSIR Research Space (South Africa)

Makondo, Ndivhuwo

2018-04-01

Full Text Available with potentially different, but unknown, kinematics from humans. This paper proposes a method that enables robots with unknown kinematics to learn skills from demonstrations. Our proposed method requires a motion trajectory obtained from human demonstrations via a...
Ještě ke slovu ještě

Czech Academy of Sciences Publication Activity Database

Nejedlý, Petr

2016-01-01

Roč. 8, č. 3 (2016), s. 38-48 ISSN 1803-876X. [Jazykovědný strukturalismus na počátku 21. století. Olomouc, 24.04.2014-24.04.2014] R&D Projects: GA ČR GAP406/10/1153 Institutional support: RVO:68378092 Keywords : Miroslav Komárek * lexical unit ještě (= still, yet) * grammatical analysis * lexicological analysis * lexical meaning Subject RIV: AI - Linguistics OBOR OECD: Linguistics
Pragmatic Frames for Teaching and Learning in Human–Robot Interaction: Review and Challenges

Science.gov (United States)

Vollmer, Anna-Lisa; Wrede, Britta; Rohlfing, Katharina J.; Oudeyer, Pierre-Yves

2016-01-01

One of the big challenges in robotics today is to learn from human users that are inexperienced in interacting with robots but yet are often used to teach skills flexibly to other humans and to children in particular. A potential route toward natural and efficient learning and teaching in Human-Robot Interaction (HRI) is to leverage the social competences of humans and the underlying interactional mechanisms. In this perspective, this article discusses the importance of pragmatic frames as flexible interaction protocols that provide important contextual cues to enable learners to infer new action or language skills and teachers to convey these cues. After defining and discussing the concept of pragmatic frames, grounded in decades of research in developmental psychology, we study a selection of HRI work in the literature which has focused on learning–teaching interaction and analyze the interactional and learning mechanisms that were used in the light of pragmatic frames. This allows us to show that many of the works have already used in practice, but not always explicitly, basic elements of the pragmatic frames machinery. However, we also show that pragmatic frames have so far been used in a very restricted way as compared to how they are used in human–human interaction and argue that this has been an obstacle preventing robust natural multi-task learning and teaching in HRI. In particular, we explain that two central features of human pragmatic frames, mostly absent of existing HRI studies, are that (1) social peers use rich repertoires of frames, potentially combined together, to convey and infer multiple kinds of cues; (2) new frames can be learnt continually, building on existing ones, and guiding the interaction toward higher levels of complexity and expressivity. To conclude, we give an outlook on the future research direction describing the relevant key challenges that need to be solved for leveraging pragmatic frames for robot learning and teaching
Uit Je Eigen Stad

NARCIS (Netherlands)

Heijden, van der P.G.M.

2015-01-01

In 2010 vatte drie ondernemers het plan op om bij de Rotterdamse Fruithaven een loods en het erom heen gelegen rangeerterrein een landbouwkundige bestemming te geven. Het duurde tot 2012 voordat voldoende kapitaal en vergunningen waren geregeld en met de aanleg van Uit Je Eigen Stad begonnen kon
Active Learning Environments with Robotic Tangibles: Children's Physical and Virtual Spatial Programming Experiences

Science.gov (United States)

Burleson, Winslow S.; Harlow, Danielle B.; Nilsen, Katherine J.; Perlin, Ken; Freed, Natalie; Jensen, Camilla Nørgaard; Lahey, Byron; Lu, Patrick; Muldner, Kasia

2018-01-01

As computational thinking becomes increasingly important for children to learn, we must develop interfaces that leverage the ways that young children learn to provide opportunities for them to develop these skills. Active Learning Environments with Robotic Tangibles (ALERT) and Robopad, an analogous on-screen virtual spatial programming…
The learning curve of robot-assisted laparoscopic aortofemoral bypass grafting for aortoiliac occlusive disease.

Science.gov (United States)

Novotný, Tomáš; Dvorák, Martin; Staffa, Robert

2011-02-01

Since the end of the 20th century, robot-assisted surgery has been finding its role among other minimally invasive methods. Vascular surgery seems to be another specialty in which the benefits of this technology can be expected. Our objective was to assess the learning curve of robot-assisted laparoscopic aortofemoral bypass grafting for aortoiliac occlusive disease in a group of 40 patients. Between May 2006 and January 2010, 40 patients (32 men, 8 women), who were a median age of 58 years (range, 48-75 years), underwent 40 robot-assisted laparoscopic aortofemoral reconstructions. Learning curve estimations were used for anastomosis, clamping, and operative time assessment. For conversion rate evaluation, the cumulative summation (CUSUM) technique was used. Statistical analysis comparing the first and second half of our group, and unilateral-to-bilateral reconstructions were performed. We created 21 aortofemoral and 19 aortobifemoral bypasses. The median proximal anastomosis time was 23 minutes (range, 18-50 minutes), median clamping time was 60 minutes (range, 40-95 minutes), and median operative time was 295 minutes (range, 180-475 minutes). The 30-day mortality rate was 0%, and no graft or wound infection or cardiopulmonary or hepatorenal complications were observed. During the median 18-month follow-up (range, 2-48 months), three early graft occlusions occurred (7%). After reoperations, the secondary patency of reconstructions was 100%. Data showed a typical short learning curve for robotic proximal anastomosis creation with anastomosis and clamping time reduction. The operative time learning curve was flat, confirming the procedure's complexity. There were two conversions to open surgery. CUSUM analysis confirmed that an acceptable conversion rate set at 5% was achieved. Comparing the first and second half of our group, all recorded times showed statistically significant improvements. Differences between unilateral and bilateral reconstructions were not
Concurrent Unimodal Learning Enhances Multisensory Responses of Bi-Directional Crossmodal Learning in Robotic Audio-Visual Tracking

DEFF Research Database (Denmark)

Shaikh, Danish; Bodenhagen, Leon; Manoonpong, Poramate

2018-01-01

modalities to independently update modality-specific neural weights on a moment-by-moment basis, in response to dynamic changes in noisy sensory stimuli. The circuit is embodied as a non-holonomic robotic agent that must orient a towards a moving audio-visual target. The circuit continuously learns the best...
Visual Semantic Navigation Based on Deep Learning for Indoor Mobile Robots

Directory of Open Access Journals (Sweden)

Li Wang

2018-01-01

Full Text Available In order to improve the environmental perception ability of mobile robots during semantic navigation, a three-layer perception framework based on transfer learning is proposed, including a place recognition model, a rotation region recognition model, and a “side” recognition model. The first model is used to recognize different regions in rooms and corridors, the second one is used to determine where the robot should be rotated, and the third one is used to decide the walking side of corridors or aisles in the room. Furthermore, the “side” recognition model can also correct the motion of robots in real time, according to which accurate arrival to the specific target is guaranteed. Moreover, semantic navigation is accomplished using only one sensor (a camera. Several experiments are conducted in a real indoor environment, demonstrating the effectiveness and robustness of the proposed perception framework.
Error amplification to promote motor learning and motivation in therapy robotics.

Science.gov (United States)

Shirzad, Navid; Van der Loos, H F Machiel

2012-01-01

To study the effects of different feedback error amplification methods on a subject's upper-limb motor learning and affect during a point-to-point reaching exercise, we developed a real-time controller for a robotic manipulandum. The reaching environment was visually distorted by implementing a thirty degrees rotation between the coordinate systems of the robot's end-effector and the visual display. Feedback error amplification was provided to subjects as they trained to learn reaching within the visually rotated environment. Error amplification was provided either visually or through both haptic and visual means, each method with two different amplification gains. Subjects' performance (i.e., trajectory error) and self-reports to a questionnaire were used to study the speed and amount of adaptation promoted by each error amplification method and subjects' emotional changes. We found that providing haptic and visual feedback promotes faster adaptation to the distortion and increases subjects' satisfaction with the task, leading to a higher level of attentiveness during the exercise. This finding can be used to design a novel exercise regimen, where alternating between error amplification methods is used to both increase a subject's motor learning and maintain a minimum level of motivational engagement in the exercise. In future experiments, we will test whether such exercise methods will lead to a faster learning time and greater motivation to pursue a therapy exercise regimen.
Filtering sensory information with XCSF: improving learning robustness and robot arm control performance.

Science.gov (United States)

Kneissler, Jan; Stalph, Patrick O; Drugowitsch, Jan; Butz, Martin V

2014-01-01

It has been shown previously that the control of a robot arm can be efficiently learned using the XCSF learning classifier system, which is a nonlinear regression system based on evolutionary computation. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we utilize the forward velocity kinematics knowledge of XCSF to alleviate the negative effect of noisy sensors for successful learning and control. We incorporate Kalman filtering for estimating successive arm positions, iteratively combining sensory readings with XCSF-based predictions of hand position changes over time. The filtered arm position is used to improve both trajectory planning and further learning of the forward velocity kinematics. We test the approach on a simulated kinematic robot arm model. The results show that the combination can improve learning and control performance significantly. However, it also shows that variance estimates of XCSF prediction may be underestimated, in which case self-delusional spiraling effects can hinder effective learning. Thus, we introduce a heuristic parameter, which can be motivated by theory, and which limits the influence of XCSF's predictions on its own further learning input. As a result, we obtain drastic improvements in noise tolerance, allowing the system to cope with more than 10 times higher noise levels.
Robotics as a resource to facilitate the learning and general skills development

Directory of Open Access Journals (Sweden)

Flor Ángela Bravo Sánchez

2012-07-01

Full Text Available Normal.dotm 0 0 1 127 729 Universidad de Salamanca 6 1 895 12.0 0 false 18 pt 18 pt 0 0 false false false /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} The growing importance of technology in the world today and its continuous development, makes the technology becomes an integral part of the formation process in childhood and youth. For this reason is important to develop proposals that are offered to children and young people to come into contact with new technologies, that is possible through the use of software and hardware tools, such as robotic prototypes and specialized programs for educational purposes This paper shows the importance of the use of robotics as a learning tool and presents the typical stages that must be confronted in implementing educational robotics projects in the classroom. It also presents an educational robotics project called "Mundo Robotica" which seeks to involve robotics in the classroom through practical activities and learning resources, all this is articulated from a virtual platform.
Indirect iterative learning control for a discrete visual servo without a camera-robot model.

Science.gov (United States)

Jiang, Ping; Bamforth, Leon C A; Feng, Zuren; Baruch, John E F; Chen, YangQuan

2007-08-01

This paper presents a discrete learning controller for vision-guided robot trajectory imitation with no prior knowledge of the camera-robot model. A teacher demonstrates a desired movement in front of a camera, and then, the robot is tasked to replay it by repetitive tracking. The imitation procedure is considered as a discrete tracking control problem in the image plane, with an unknown and time-varying image Jacobian matrix. Instead of updating the control signal directly, as is usually done in iterative learning control (ILC), a series of neural networks are used to approximate the unknown Jacobian matrix around every sample point in the demonstrated trajectory, and the time-varying weights of local neural networks are identified through repetitive tracking, i.e., indirect ILC. This makes repetitive segmented training possible, and a segmented training strategy is presented to retain the training trajectories solely within the effective region for neural network approximation. However, a singularity problem may occur if an unmodified neural-network-based Jacobian estimation is used to calculate the robot end-effector velocity. A new weight modification algorithm is proposed which ensures invertibility of the estimation, thus circumventing the problem. Stability is further discussed, and the relationship between the approximation capability of the neural network and the tracking accuracy is obtained. Simulations and experiments are carried out to illustrate the validity of the proposed controller for trajectory imitation of robot manipulators with unknown time-varying Jacobian matrices.
An approach to robot SLAM based on incremental appearance learning with omnidirectional vision

Science.gov (United States)

Wu, Hua; Qin, Shi-Yin

2011-03-01

Localisation and mapping with an omnidirectional camera becomes more difficult as the landmark appearances change dramatically in the omnidirectional image. With conventional techniques, it is difficult to match the features of the landmark with the template. We present a novel robot simultaneous localisation and mapping (SLAM) algorithm with an omnidirectional camera, which uses incremental landmark appearance learning to provide posterior probability distribution for estimating the robot pose under a particle filtering framework. The major contribution of our work is to represent the posterior estimation of the robot pose by incremental probabilistic principal component analysis, which can be naturally incorporated into the particle filtering algorithm for robot SLAM. Moreover, the innovative method of this article allows the adoption of the severe distorted landmark appearances viewed with omnidirectional camera for robot SLAM. The experimental results demonstrate that the localisation error is less than 1 cm in an indoor environment using five landmarks, and the location of the landmark appearances can be estimated within 5 pixels deviation from the ground truth in the omnidirectional image at a fairly fast speed.

Reinforcement learning for a biped robot based on a CPG-actor-critic method.

Science.gov (United States)

Nakamura, Yutaka; Mori, Takeshi; Sato, Masa-aki; Ishii, Shin

2007-08-01

Animals' rhythmic movements, such as locomotion, are considered to be controlled by neural circuits called central pattern generators (CPGs), which generate oscillatory signals. Motivated by this biological mechanism, studies have been conducted on the rhythmic movements controlled by CPG. As an autonomous learning framework for a CPG controller, we propose in this article a reinforcement learning method we call the "CPG-actor-critic" method. This method introduces a new architecture to the actor, and its training is roughly based on a stochastic policy gradient algorithm presented recently. We apply this method to an automatic acquisition problem of control for a biped robot. Computer simulations show that training of the CPG can be successfully performed by our method, thus allowing the biped robot to not only walk stably but also adapt to environmental changes.
Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

Directory of Open Access Journals (Sweden)

Min Wang

2017-01-01

Full Text Available A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF neural network (NN approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation (PE condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.
Emotion in reinforcement learning agents and robots: A survey

OpenAIRE

Moerland, T.M.; Broekens, D.J.; Jonker, C.M.

2018-01-01

This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are usually grounded in the agent's decision making architecture, of which RL is an important subclass. Studying emotions in RL-based agents is useful for ...
Programming Robots with Associative Memories

Energy Technology Data Exchange (ETDEWEB)

Touzet, C

1999-07-10

Today, there are several drawbacks that impede the necessary and much needed use of robot learning techniques in real applications. First, the time needed to achieve the synthesis of any behavior is prohibitive. Second, the robot behavior during the learning phase is "by definition" bad, it may even be dangerous. Third, except within the lazy learning approach, a new behavior implies a new learning phase. We propose in this paper to use self-organizing maps to encode the non explicit model of the robot-world interaction sampled by the lazy memory, and then generate a robot behavior by means of situations to be achieved, i.e., points on the self-organizing maps. Any behavior can instantaneously be synthesized by the definition of a goal situation. Its performance will be minimal (not evidently bad) and will improve by the mere repetition of the behavior.
Programming Robots with Associative Memories

International Nuclear Information System (INIS)

Touzet, C.

1999-01-01

Today, there are several drawbacks that impede the necessary and much needed use of robot learning techniques in real applications. First, the time needed to achieve the synthesis of any behavior is prohibitive. Second, the robot behavior during the learning phase is by definition bad, it may even be dangerous. Third, except within the lazy learning approach, a new behavior implies a new learning phase. We propose in this paper to use self-organizing maps to encode the non explicit model of the robot-world interaction sampled by the lazy memory, and then generate a robot behavior by means of situations to be achieved, i.e., points on the self-organizing maps. Any behavior can instantaneously be synthesized by the definition of a goal situation. Its performance will be minimal (not evidently bad) and will improve by the mere repetition of the behavior
Lessons learned over a decade of pediatric robotic ureteral reimplantation

Directory of Open Access Journals (Sweden)

Minki Baek

2017-01-01

Full Text Available The da Vinci robotic system has improved surgeon dexterity, ergonomics, and visualization to allow for a minimally invasive option for complex reconstructive procedures in children. Over the past decade, robot-assisted laparoscopic ureteral reimplantation (RALUR has become a viable minimally invasive surgical option for pediatric vesicoureteral reflux (VUR. However, higher-thanexpected complication rates and suboptimal reflux resolution rates at some centers have also been reported. The heterogeneity of surgical outcomes may arise from the inherent and underestimated complexity of the RALUR procedure that may justify its reclassification as a complex reconstructive procedure and especially for robotic surgeons early in their learning curve. Currently, no consensus exists on the role of RALUR for the surgical management of VUR. High success rates and low major complication rates are the expected norm for the current gold standard surgical option of open ureteral reimplantation. Similar to how robot-assisted laparoscopic surgery has gradually replaced open surgery as the most utilized option for prostatectomy in prostate cancer patients, RALUR may become a higher utilized surgical option in children with VUR if the adoption of standardized surgical techniques that have been associated with optimal outcomes can be adopted during the second decade of RALUR. A future standard of RALUR for children with VUR whose parents seek a minimally invasive surgical option can arise if widespread achievement of high success rates and low major complication rates can be obtained, similar to the replacement of open surgery with robot-assisted laparoscopic radical prostectomy as the new strandard for men with prostate cancer.
Child, Robot and Educational Material : A Triadic Interaction

NARCIS (Netherlands)

Davison, Daniel Patrick

The process in which a child and a robot work together to solve a learning task can be characterised as a triadic interaction. Interactions between the child and robot; the child and learning materials; and the robot and learning materials will each shape the perception and appreciation the child
Child, Robot and Educational Material: A Triadic Interaction

NARCIS (Netherlands)

Davison, Daniel Patrick

The process in which a child and a robot work together to solve a learning task can be characterised as a triadic interaction. Interactions between the child and robot; the child and learning materials; and the robot and learning materials will each shape the perception and appreciation the child
Optimal critic learning for robot control in time-varying environments.

Science.gov (United States)

Wang, Chen; Li, Yanan; Ge, Shuzhi Sam; Lee, Tong Heng

2015-10-01

In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.
An Architecture for Emotional and Context-Aware Associative Learning for Robot Companions

OpenAIRE

Rizzi Raymundo, C.; Johnson, C. G.; Vargas, P. A.

2015-01-01

This work proposes a theoretical architectural model based on the brain's fear learning system with the purpose of generating artificial fear conditioning at both stimuli and context abstraction levels in robot companions. The proposed architecture is inspired by the different brain regions involved in fear learning, here divided into four modules that work in an integrated and parallel manner: the sensory system, the amygdala system, the hippocampal system and the working memory. Each of the...
De digitale stresscoach : totale controle over je mentale gezondheid of Big Brother is watching you?

NARCIS (Netherlands)

Lieshout, M. van; Wiezer, N.; Korte, E. de

2014-01-01

Een digitale coach die je helpt stress op je werk te verminderen en meer energie uit je werk te halen klinkt aantrekkelijk. Stress ervaren we immers allemaal. Net zoals we allemaal streven naar werk waar je energie van krijgt. En stress vermijden we liever. Stress is een maatschappelijk probleem en
De Informatiefuik : hoeveel controle heb jij over je online identiteit?

NARCIS (Netherlands)

Roosendaal, A.P.C.

2013-01-01

Bijna iedereen in Nederland heeft een 'digitaal profiel', bijvoorbeeld op Facebook of LinkedIn. Maar ook anderen werken aan een profiel van jou: denk aan je bestellijst bij Bol.com of een bestand van de overheid. In feite wordt bijna al je klikgedrag op internet wel door iemand opgeslagen en
Study and Application of Reinforcement Learning in Cooperative Strategy of the Robot Soccer Based on BDI Model

Directory of Open Access Journals (Sweden)

Wu Bo-ying

2009-11-01

Full Text Available The dynamic cooperation model of multi-Agent is formed by combining reinforcement learning with BDI model. In this model, the concept of the individual optimization loses its meaning, because the repayment of each Agent dose not only depend on itsself but also on the choice of other Agents. All Agents can pursue a common optimum solution and try to realize the united intention as a whole to a maximum limit. The robot moves to its goal, depending on the present positions of the other robots that cooperate with it and the present position of the ball. One of these robots cooperating with it is controlled to move by man with a joystick. In this way, Agent can be ensured to search for each state-action as frequently as possible when it carries on choosing movements, so as to shorten the time of searching for the movement space so that the convergence speed of reinforcement learning can be improved. The validity of the proposed cooperative strategy for the robot soccer has been proved by combining theoretical analysis with simulation robot soccer match (11vs11 .
Learning to Automatically Detect Features for Mobile Robots Using Second-Order Hidden Markov Models

Directory of Open Access Journals (Sweden)

Olivier Aycard

2004-12-01

Full Text Available In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.
Why Are There Developmental Stages in Language Learning? A Developmental Robotics Model of Language Development.

Science.gov (United States)

Morse, Anthony F; Cangelosi, Angelo

2017-02-01

Most theories of learning would predict a gradual acquisition and refinement of skills as learning progresses, and while some highlight exponential growth, this fails to explain why natural cognitive development typically progresses in stages. Models that do span multiple developmental stages typically have parameters to "switch" between stages. We argue that by taking an embodied view, the interaction between learning mechanisms, the resulting behavior of the agent, and the opportunities for learning that the environment provides can account for the stage-wise development of cognitive abilities. We summarize work relevant to this hypothesis and suggest two simple mechanisms that account for some developmental transitions: neural readiness focuses on changes in the neural substrate resulting from ongoing learning, and perceptual readiness focuses on the perceptual requirements for learning new tasks. Previous work has demonstrated these mechanisms in replications of a wide variety of infant language experiments, spanning multiple developmental stages. Here we piece this work together as a single model of ongoing learning with no parameter changes at all. The model, an instance of the Epigenetic Robotics Architecture (Morse et al 2010) embodied on the iCub humanoid robot, exhibits ongoing multi-stage development while learning pre-linguistic and then basic language skills. Copyright © 2016 Cognitive Science Society, Inc.
An Integrated Framework for Human-Robot Collaborative Manipulation.

Science.gov (United States)

Sheng, Weihua; Thobbi, Anand; Gu, Ye

2015-10-01

This paper presents an integrated learning framework that enables humanoid robots to perform human-robot collaborative manipulation tasks. Specifically, a table-lifting task performed jointly by a human and a humanoid robot is chosen for validation purpose. The proposed framework is split into two phases: 1) phase I-learning to grasp the table and 2) phase II-learning to perform the manipulation task. An imitation learning approach is proposed for phase I. In phase II, the behavior of the robot is controlled by a combination of two types of controllers: 1) reactive and 2) proactive. The reactive controller lets the robot take a reactive control action to make the table horizontal. The proactive controller lets the robot take proactive actions based on human motion prediction. A measure of confidence of the prediction is also generated by the motion predictor. This confidence measure determines the leader/follower behavior of the robot. Hence, the robot can autonomously switch between the behaviors during the task. Finally, the performance of the human-robot team carrying out the collaborative manipulation task is experimentally evaluated on a platform consisting of a Nao humanoid robot and a Vicon motion capture system. Results show that the proposed framework can enable the robot to carry out the collaborative manipulation task successfully.
[The medical theory of Lee Je-ma and its character].

Science.gov (United States)

Lee, Kyung-Lock

2005-12-01

Lee Je-ma 1837-1900) was a prominent scholar as well as an Korean physician. classified every people into four distinctive types: greater yang [tai yang] person, lesser yin [shao yin] person, greater yin [tai yin] person, lesser yin [shao yin] person. This theory would dictate proper treatment for each type in accordance with individual differences of physical and temperament features. Using these four types he created The Medical Science of Four Types. This article is intended to look into the connection between Lee Je-Ma's 'The Medical Science of Four Types' and 'The Modern' with organizing his ideas about the human body and the human being. Through The Modern, the theory of human being underwent a complete change. Human being in The Premodern, which was determined by sex, age and social status has been changed to the individual human being, which is featured by equality. Lee Je-Ma's medical theory of The Medical Science of Four Types would be analyzed as follow. His concept of human body is oriented toward observable objectivity. But on the other hand, it still remains transcendent status of medical science, which is subordinated by philosophy. According to Lee Je-Ma's theory of human being, human is an equal individual in a modern way of thinking, not as a part of hierarchical group. But on the other hand, it still remains incomplete from getting rid of morality aspect that includes virtue and vice in the concept of human body. The common factors in Lee Je-Ma's ideas about the human body and the human being is 'Dualism of mind and body that means all kinds of status and results depends on each individual. As is stated above, Lee Je-Ma's medical theory has many aspects of The Modern and it proves that Korean traditional medicine could be modernized by itself.
Starting a robotic program in general thoracic surgery: why, how, and lessons learned.

Science.gov (United States)

Cerfolio, Robert J; Bryant, Ayesha S; Minnich, Douglas J

2011-06-01

We report our experience in starting a robotic program in thoracic surgery. We retrospectively reviewed our experience in starting a robotic program in general thoracic surgery on a consecutive series of patients. Between February 2009 and September 2010, 150 patients underwent robotic operations. Types of procedures were lobectomy in 62, thymectomy in 30, and benign esophageal procedures in 6. No thymectomy or esophageal procedures required conversion. One conversion was needed for suspected bleeding for a mediastinal mass. Twelve patients were converted for lobectomy (none for bleeding, 1 in the last 24). Median operative time for robotic thymectomy was 119 minutes, and median length of stay was 1 day. The median time for robotic lobectomy was 185 minutes, and median length of stay was 2 days. There were no operative deaths. Morbidity occurred in 23 patients (15%). All patients with cancer had R0 resections and resection of all visible mediastinal and hilar lymph nodes. Robotic surgery is safe and oncologically sound. It requires training of the entire operating room team. The learning curve is steep, involving port placement, availability of the proper instrumentation, use of the correct robotic arms, and proper patient positioning. The robot provides an ideal surgical approach for thymectomy and other mediastinal tumors. Its advantage over thoracoscopy for pulmonary resection is unproven; however, we believe complete thoracic lymph node dissection and teaching is easier. Importantly, defined credentialing for surgeons and cost analysis studies are needed. Copyright © 2011 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.
Je mihule ukrajinská ještě součástí naší fauny?

Czech Academy of Sciences Publication Activity Database

Hanel, L.; Lusk, Stanislav

2013-01-01

Roč. 61, č. 6 (2013), s. 279-281 ISSN 0044-4812 Institutional support: RVO:68081766 Keywords : lamprey Subject RIV: EG - Zoology http://ziva.avcr.cz/2013-6/je-mihule-ukrajinska-jeste-soucasti-nasi-fauny.html
Detecting and Classifying Human Touches in a Social Robot Through Acoustic Sensing and Machine Learning.

Science.gov (United States)

Alonso-Martín, Fernando; Gamboa-Montero, Juan José; Castillo, José Carlos; Castro-González, Álvaro; Salichs, Miguel Ángel

2017-05-16

An important aspect in Human-Robot Interaction is responding to different kinds of touch stimuli. To date, several technologies have been explored to determine how a touch is perceived by a social robot, usually placing a large number of sensors throughout the robot's shell. In this work, we introduce a novel approach, where the audio acquired from contact microphones located in the robot's shell is processed using machine learning techniques to distinguish between different types of touches. The system is able to determine when the robot is touched (touch detection), and to ascertain the kind of touch performed among a set of possibilities: stroke , tap , slap , and tickle (touch classification). This proposal is cost-effective since just a few microphones are able to cover the whole robot's shell since a single microphone is enough to cover each solid part of the robot. Besides, it is easy to install and configure as it just requires a contact surface to attach the microphone to the robot's shell and plug it into the robot's computer. Results show the high accuracy scores in touch gesture recognition. The testing phase revealed that Logistic Model Trees achieved the best performance, with an F -score of 0.81. The dataset was built with information from 25 participants performing a total of 1981 touch gestures.

Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions.

Science.gov (United States)

Kassahun, Yohannes; Yu, Bingbin; Tibebu, Abraham Temesgen; Stoyanov, Danail; Giannarou, Stamatia; Metzen, Jan Hendrik; Vander Poorten, Emmanuel

2016-04-01

Advances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room. The review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive. Studies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices. ML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the state of the art in surgical
Learning and Chaining of Motor Primitives for Goal-directed Locomotion of a Snake-Like Robot with Screw-Drive Units

DEFF Research Database (Denmark)

Chatterjee, Sromona; Nachstedt, Timo; Tamosiunaite, Minija

2015-01-01

-directed locomotion for the robot. The behavioural primitives of the robot are generated using a reinforcement learning approach called "Policy Improvement with Path Integrals" (PI2). PI2 is numerically simple and has the ability to deal with high-dimensional systems. Here, PI2 is used to learn the robot’s motor...... controls by finding proper locomotion control parameters, like joint angles and screw-drive unit velocities, in a coordinated manner for different goals. Thus, it is able to generate a large repertoire of motor primitives, which are selectively stored to form a primitive library. The learning process...
Interactive robots in experimental biology.

Science.gov (United States)

Krause, Jens; Winfield, Alan F T; Deneubourg, Jean-Louis

2011-07-01

Interactive robots have the potential to revolutionise the study of social behaviour because they provide several methodological advances. In interactions with live animals, the behaviour of robots can be standardised, morphology and behaviour can be decoupled (so that different morphologies and behavioural strategies can be combined), behaviour can be manipulated in complex interaction sequences and models of behaviour can be embodied by the robot and thereby be tested. Furthermore, robots can be used as demonstrators in experiments on social learning. As we discuss here, the opportunities that robots create for new experimental approaches have far-reaching consequences for research in fields such as mate choice, cooperation, social learning, personality studies and collective behaviour. Copyright © 2011 Elsevier Ltd. All rights reserved.
Peripersonal Space and Margin of Safety around the Body: Learning Visuo-Tactile Associations in a Humanoid Robot with Artificial Skin.

Science.gov (United States)

Roncone, Alessandro; Hoffmann, Matej; Pattacini, Ugo; Fadiga, Luciano; Metta, Giorgio

2016-01-01

This paper investigates a biologically motivated model of peripersonal space through its implementation on a humanoid robot. Guided by the present understanding of the neurophysiology of the fronto-parietal system, we developed a computational model inspired by the receptive fields of polymodal neurons identified, for example, in brain areas F4 and VIP. The experiments on the iCub humanoid robot show that the peripersonal space representation i) can be learned efficiently and in real-time via a simple interaction with the robot, ii) can lead to the generation of behaviors like avoidance and reaching, and iii) can contribute to the understanding the biological principle of motor equivalence. More specifically, with respect to i) the present model contributes to hypothesizing a learning mechanisms for peripersonal space. In relation to point ii) we show how a relatively simple controller can exploit the learned receptive fields to generate either avoidance or reaching of an incoming stimulus and for iii) we show how the robot can select arbitrary body parts as the controlled end-point of an avoidance or reaching movement.
Performance Comparison of Two Reinforcement Learning Algorithms for Small Mobile Robots

Czech Academy of Sciences Publication Activity Database

Neruda, Roman; Slušný, Stanislav

2009-01-01

Roč. 2, č. 1 (2009), s. 59-68 ISSN 2005-4297 R&D Projects: GA MŠk(CZ) 1M0567 Grant - others:GA UK(CZ) 7637/2007 Institutional research plan: CEZ:AV0Z10300504 Keywords : reinforcement learning * mobile robots * inteligent agents Subject RIV: IN - Informatics, Computer Science http://www.sersc.org/journals/IJCA/vol2_no1/7.pdf
Studying social robots in practiced places

DEFF Research Database (Denmark)

Hasse, Cathrine; Bruun, Maja Hojer; Hanghøj, Signe

2015-01-01

values, social relations and materialities. Though substantial funding has been invested in developing health service robots, few studies have been undertaken that explore human-robot interactions as they play out in everyday practice. We argue that the complex learning processes involve not only so...... of technologies in use, e.g., technologies as multistable ontologies. The argument builds on an empirical study of robots at a Danish rehabilitation centre. Ethnographic methods combined with anthropological learning processes open up new way for exploring how robots enter into professional practices and change...
Lessons Learned in Designing User-configurable Modular Robotics

DEFF Research Database (Denmark)

Lund, Henrik Hautop

2013-01-01

User-configurable robotics allows users to easily configure robotic systems to perform task-fulfilling behaviors as desired by the users. With a user configurable robotic system, the user can easily modify the physical and func-tional aspect in terms of hardware and software components of a robotic...... with the semi-autonomous com-ponents of the user-configurable robotic system in interaction with the given environment. Components constituting such a user-configurable robotic system can be characterized as modules in a modular robotic system. Several factors in the definition and implementation...
Learning in robotic manipulation: The role of dimensionality reduction in policy search methods. Comment on "Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands" by Marco Santello et al.

Science.gov (United States)

Ficuciello, Fanny; Siciliano, Bruno

2016-07-01

A question that often arises, among researchers working on artificial hands and robotic manipulation, concerns the real meaning of synergies. Namely, are they a realistic representation of the central nervous system control of manipulation activities at different levels and of the sensory-motor manipulation apparatus of the human being, or do they constitute just a theoretical framework exploiting analytical methods to simplify the representation of grasping and manipulation activities? Apparently, this is not a simple question to answer and, in this regard, many minds from the field of neuroscience and robotics are addressing the issue [1]. The interest of robotics is definitely oriented towards the adoption of synergies to tackle the control problem of devices with high number of degrees of freedom (DoFs) which are required to achieve motor and learning skills comparable to those of humans. The synergy concept is useful for innovative underactuated design of anthropomorphic hands [2], while the resulting dimensionality reduction simplifies the control of biomedical devices such as myoelectric hand prostheses [3]. Synergies might also be useful in conjunction with the learning process [4]. This aspect is less explored since few works on synergy-based learning have been realized in robotics. In learning new tasks through trial-and-error, physical interaction is important. On the other hand, advanced mechanical designs such as tendon-driven actuation, underactuated compliant mechanisms and hyper-redundant/continuum robots might exhibit enhanced capabilities of adapting to changing environments and learning from exploration. In particular, high DoFs and compliance increase the complexity of modelling and control of these devices. An analytical approach to manipulation planning requires a precise model of the object, an accurate description of the task, and an evaluation of the object affordance, which all make the process rather time consuming. The integration of
Comparison of haptic guidance and error amplification robotic trainings for the learning of a timing-based motor task by healthy seniors.

Science.gov (United States)

Bouchard, Amy E; Corriveau, Hélène; Milot, Marie-Hélène

2015-01-01

With age, a decline in the temporal aspect of movement is observed such as a longer movement execution time and a decreased timing accuracy. Robotic training can represent an interesting approach to help improve movement timing among the elderly. Two types of robotic training-haptic guidance (HG; demonstrating the correct movement for a better movement planning and improved execution of movement) and error amplification (EA; exaggerating movement errors to have a more rapid and complete learning) have been positively used in young healthy subjects to boost timing accuracy. For healthy seniors, only HG training has been used so far where significant and positive timing gains have been obtained. The goal of the study was to evaluate and compare the impact of both HG and EA robotic trainings on the improvement of seniors' movement timing. Thirty-two healthy seniors (mean age 68 ± 4 years) learned to play a pinball-like game by triggering a one-degree-of-freedom hand robot at the proper time to make a flipper move and direct a falling ball toward a randomly positioned target. During HG and EA robotic trainings, the subjects' timing errors were decreased and increased, respectively, based on the subjects' timing errors in initiating a movement. Results showed that only HG training benefited learning, but the improvement did not generalize to untrained targets. Also, age had no influence on the efficacy of HG robotic training, meaning that the oldest subjects did not benefit more from HG training than the younger senior subjects. Using HG to teach the correct timing of movement seems to be a good strategy to improve motor learning for the elderly as for younger people. However, more studies are needed to assess the long-term impact of HG robotic training on improvement in movement timing.
A Passive Learning Sensor Architecture for Multimodal Image Labeling: An Application for Social Robots

Directory of Open Access Journals (Sweden)

Marco A. Gutiérrez

2017-02-01

Full Text Available Object detection and classification have countless applications in human–robot interacting systems. It is a necessary skill for autonomous robots that perform tasks in household scenarios. Despite the great advances in deep learning and computer vision, social robots performing non-trivial tasks usually spend most of their time finding and modeling objects. Working in real scenarios means dealing with constant environment changes and relatively low-quality sensor data due to the distance at which objects are often found. Ambient intelligence systems equipped with different sensors can also benefit from the ability to find objects, enabling them to inform humans about their location. For these applications to succeed, systems need to detect the objects that may potentially contain other objects, working with relatively low-resolution sensor data. A passive learning architecture for sensors has been designed in order to take advantage of multimodal information, obtained using an RGB-D camera and trained semantic language models. The main contribution of the architecture lies in the improvement of the performance of the sensor under conditions of low resolution and high light variations using a combination of image labeling and word semantics. The tests performed on each of the stages of the architecture compare this solution with current research labeling techniques for the application of an autonomous social robot working in an apartment. The results obtained demonstrate that the proposed sensor architecture outperforms state-of-the-art approaches.
The medical theory of Lee Je-ma and its character

Directory of Open Access Journals (Sweden)

LEE Kyung-Lock

2005-12-01

Full Text Available Lee Je-ma(李濟馬, 1837-1900 was a prominent scholar as well as an Korean physician He classified every people into four distinctive types: greater yang[tai yang] person, lesser yin[shao yin] person greater yin[tai yin] person, lesser yin[shao yin] person. This theory would dictate proper treatment for each type in accordance with individual differences of physical and temperament features Using these four types he created The Medical Science of Four Types(四象體質論.This article is intended to look into the connection between Lee Je-Ma's 'The Medical Science of Four Types' and 'The Modern' with organizing his ideas about the human body and the human being. Through The Modern, the theory of human being(人間觀 underwent a complete change. Human being in The Premodern, which was determined by sex, age and social status has been changed to the individual human being, which is featured by equality. Lee Je-Ma's medical theory of The Medical Science of Four Types would be analyzed as follow. His concept of human body(人體論 is oriented toward observable objectivity. But on the other hand, it still remains transcendent status of medical science, which is subordinated by philosophy According to Lee Je-Ma's theory of human being human is an equal individual in a modern way of thinking not as a part of hierarchical group. But on the other hand, it still remains incomplete from getting rid of morality aspect that includes virtue and vice in the concept of human body.The common factors in Lee Je-Ma's ideas about the human body and the human being is 'Dualism of mind and body(心身二元論' that means all kinds of status and results depends on each individual. As is stated above, Lee Je-Ma's medical theory has many aspects of The Modern and it proves that Korean traditional medicine could be modernized by itself.
Job evaluation for clinical nursing jobs by implementing the NHS JE system.

Science.gov (United States)

Kahya, Emin; Oral, Nurten

2007-10-01

The purpose of this paper was to evaluate locally all the clinical nursing jobs implementing the NHS JE system in four hospitals. The NHS JE was developed by the Department of Health in the UK in 2003-2004. A job analysis questionnaire was designed to gather current job descriptions. It was distributed to each of 158 clinical nurses and supervisor nurses in 31 variety clinics at four hospitals in one city. The questionnaires were analysed to evaluate locally all the identified 94 nursing jobs. Fourteen of 19 nursing jobs in the medical and surgical clinics can be matched to the nurse national job in the NHS JE system. The results indicated that two new nursing jobs titled nurse B and nurse advanced B should be added to the list of national nursing jobs in the NHS JE system.
Flat vs. Expressive Storytelling: Young Children's Learning and Retention of a Social Robot's Narrative.

Science.gov (United States)

Kory Westlund, Jacqueline M; Jeong, Sooyeon; Park, Hae W; Ronfard, Samuel; Adhikari, Aradhana; Harris, Paul L; DeSteno, David; Breazeal, Cynthia L

2017-01-01

Prior research with preschool children has established that dialogic or active book reading is an effective method for expanding young children's vocabulary. In this exploratory study, we asked whether similar benefits are observed when a robot engages in dialogic reading with preschoolers. Given the established effectiveness of active reading, we also asked whether this effectiveness was critically dependent on the expressive characteristics of the robot. For approximately half the children, the robot's active reading was expressive; the robot's voice included a wide range of intonation and emotion ( Expressive ). For the remaining children, the robot read and conversed with a flat voice, which sounded similar to a classic text-to-speech engine and had little dynamic range ( Flat ). The robot's movements were kept constant across conditions. We performed a verification study using Amazon Mechanical Turk (AMT) to confirm that the Expressive robot was viewed as significantly more expressive, more emotional, and less passive than the Flat robot. We invited 45 preschoolers with an average age of 5 years who were either English Language Learners (ELL), bilingual, or native English speakers to engage in the reading task with the robot. The robot narrated a story from a picture book, using active reading techniques and including a set of target vocabulary words in the narration. Children were post-tested on the vocabulary words and were also asked to retell the story to a puppet. A subset of 34 children performed a second story retelling 4-6 weeks later. Children reported liking and learning from the robot a similar amount in the Expressive and Flat conditions. However, as compared to children in the Flat condition, children in the Expressive condition were more concentrated and engaged as indexed by their facial expressions; they emulated the robot's story more in their story retells; and they told longer stories during their delayed retelling. Furthermore, children who
Robotic Construction Kits as Computational Manipulatives for Learning in the STEM Disciplines

Science.gov (United States)

Sullivan, Florence R.; Heffernan, John

2016-01-01

This article presents a systematic review of research related to the use of robotics construction kits (RCKs) in P-12 learning in the STEM disciplines for typically developing children. The purpose of this review is to configure primarily qualitative and mixed methods findings from studies meeting our selection and quality criterion to answer the…
Remotely controlling of mobile robots using gesture captured by the Kinect and recognized by machine learning method

Science.gov (United States)

Hsu, Roy CHaoming; Jian, Jhih-Wei; Lin, Chih-Chuan; Lai, Chien-Hung; Liu, Cheng-Ting

2013-01-01

The main purpose of this paper is to use machine learning method and Kinect and its body sensation technology to design a simple, convenient, yet effective robot remote control system. In this study, a Kinect sensor is used to capture the human body skeleton with depth information, and a gesture training and identification method is designed using the back propagation neural network to remotely command a mobile robot for certain actions via the Bluetooth. The experimental results show that the designed mobile robots remote control system can achieve, on an average, more than 96% of accurate identification of 7 types of gestures and can effectively control a real e-puck robot for the designed commands.
Feedback error learning controller for functional electrical stimulation assistance in a hybrid robotic system for reaching rehabilitation

Directory of Open Access Journals (Sweden)

Francisco Resquín

2016-07-01

Full Text Available Hybrid robotic systems represent a novel research field, where functional electrical stimulation (FES is combined with a robotic device for rehabilitation of motor impairment. Under this approach, the design of robust FES controllers still remains an open challenge. In this work, we aimed at developing a learning FES controller to assist in the performance of reaching movements in a simple hybrid robotic system setting. We implemented a Feedback Error Learning (FEL control strategy consisting of a feedback PID controller and a feedforward controller based on a neural network. A passive exoskeleton complemented the FES controller by compensating the effects of gravity. We carried out experiments with healthy subjects to validate the performance of the system. Results show that the FEL control strategy is able to adjust the FES intensity to track the desired trajectory accurately without the need of a previous mathematical model.
Nonparametric Online Learning Control for Soft Continuum Robot: An Enabling Technique for Effective Endoscopic Navigation

Science.gov (United States)

Lee, Kit-Hang; Fu, Denny K.C.; Leong, Martin C.W.; Chow, Marco; Fu, Hing-Choi; Althoefer, Kaspar; Sze, Kam Yim; Yeung, Chung-Kwong

2017-01-01

Abstract Bioinspired robotic structures comprising soft actuation units have attracted increasing research interest. Taking advantage of its inherent compliance, soft robots can assure safe interaction with external environments, provided that precise and effective manipulation could be achieved. Endoscopy is a typical application. However, previous model-based control approaches often require simplified geometric assumptions on the soft manipulator, but which could be very inaccurate in the presence of unmodeled external interaction forces. In this study, we propose a generic control framework based on nonparametric and online, as well as local, training to learn the inverse model directly, without prior knowledge of the robot's structural parameters. Detailed experimental evaluation was conducted on a soft robot prototype with control redundancy, performing trajectory tracking in dynamically constrained environments. Advanced element formulation of finite element analysis is employed to initialize the control policy, hence eliminating the need for random exploration in the robot's workspace. The proposed control framework enabled a soft fluid-driven continuum robot to follow a 3D trajectory precisely, even under dynamic external disturbance. Such enhanced control accuracy and adaptability would facilitate effective endoscopic navigation in complex and changing environments. PMID:29251567
Automatic learning rate adjustment for self-supervising autonomous robot control

Science.gov (United States)

Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

1992-01-01

Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.
A Robot-Partner for Preschool Children Learning English Using Socio-Cognitive Conflict

Science.gov (United States)

Mazzoni, Elvis; Benvenuti, Martina

2015-01-01

This paper presents an exploratory study in which a humanoid robot (MecWilly) acted as a partner to preschool children, helping them to learn English words. In order to use the Socio-Cognitive Conflict paradigm to induce the knowledge acquisition process, we designed a playful activity in which children worked in pairs with another child or with…
Correction of Visual Perception Based on Neuro-Fuzzy Learning for the Humanoid Robot TEO

Directory of Open Access Journals (Sweden)

Juan Hernandez-Vicen

2018-03-01

Full Text Available New applications related to robotic manipulation or transportation tasks, with or without physical grasping, are continuously being developed. To perform these activities, the robot takes advantage of different kinds of perceptions. One of the key perceptions in robotics is vision. However, some problems related to image processing makes the application of visual information within robot control algorithms difficult. Camera-based systems have inherent errors that affect the quality and reliability of the information obtained. The need of correcting image distortion slows down image parameter computing, which decreases performance of control algorithms. In this paper, a new approach to correcting several sources of visual distortions on images in only one computing step is proposed. The goal of this system/algorithm is the computation of the tilt angle of an object transported by a robot, minimizing image inherent errors and increasing computing speed. After capturing the image, the computer system extracts the angle using a Fuzzy filter that corrects at the same time all possible distortions, obtaining the real angle in only one processing step. This filter has been developed by the means of Neuro-Fuzzy learning techniques, using datasets with information obtained from real experiments. In this way, the computing time has been decreased and the performance of the application has been improved. The resulting algorithm has been tried out experimentally in robot transportation tasks in the humanoid robot TEO (Task Environment Operator from the University Carlos III of Madrid.

Interactive Exploration Robots: Human-Robotic Collaboration and Interactions

Science.gov (United States)

Fong, Terry

2017-01-01

For decades, NASA has employed different operational approaches for human and robotic missions. Human spaceflight missions to the Moon and in low Earth orbit have relied upon near-continuous communication with minimal time delays. During these missions, astronauts and mission control communicate interactively to perform tasks and resolve problems in real-time. In contrast, deep-space robotic missions are designed for operations in the presence of significant communication delay - from tens of minutes to hours. Consequently, robotic missions typically employ meticulously scripted and validated command sequences that are intermittently uplinked to the robot for independent execution over long periods. Over the next few years, however, we will see increasing use of robots that blend these two operational approaches. These interactive exploration robots will be remotely operated by humans on Earth or from a spacecraft. These robots will be used to support astronauts on the International Space Station (ISS), to conduct new missions to the Moon, and potentially to enable remote exploration of planetary surfaces in real-time. In this talk, I will discuss the technical challenges associated with building and operating robots in this manner, along with lessons learned from research conducted with the ISS and in the field.
Lessons learned from the STS-120/ISS 10A robotics operations

Science.gov (United States)

Aziz, Sarmad

2010-01-01

analysis of the robotics operations executed during the STS-120 and 10A stage mission. The paper will highlight the unique challenges associated with the planning and execution of the P6 truss and Node 2 module relocation tasks, as well as the robotics issues faced during the planning and execution of the solar array repair space walk. The analysis will address the operational techniques and mission planning guideline used to deal with tight timelines, structural loads issues, ISS attitude control issues, and complex interdependencies between various ISS systems during the assembly and solar array repair operations. A discussion of the lessons learned from the planning and execution of these complex robotics tasks will also be presented.
Marine Robot Autonomy

CERN Document Server

2013-01-01

Autonomy for Marine Robots provides a timely and insightful overview of intelligent autonomy in marine robots. A brief history of this emerging field is provided, along with a discussion of the challenges unique to the underwater environment and their impact on the level of intelligent autonomy required. Topics covered at length examine advanced frameworks, path-planning, fault tolerance, machine learning, and cooperation as relevant to marine robots that need intelligent autonomy. This book also: Discusses and offers solutions for the unique challenges presented by more complex missions and the dynamic underwater environment when operating autonomous marine robots Includes case studies that demonstrate intelligent autonomy in marine robots to perform underwater simultaneous localization and mapping Autonomy for Marine Robots is an ideal book for researchers and engineers interested in the field of marine robots.
Students Learn Programming Faster through Robotic Simulation

Science.gov (United States)

Liu, Allison; Newsom, Jeff; Schunn, Chris; Shoop, Robin

2013-01-01

Schools everywhere are using robotics education to engage kids in applied science, technology, engineering, and mathematics (STEM) activities, but teaching programming can be challenging due to lack of resources. This article reports on using Robot Virtual Worlds (RVW) and curriculum available on the Internet to teach robot programming. It also…
Towards a synergy framework across neuroscience and robotics: Lessons learned and open questions. Reply to comments on: "Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands"

Science.gov (United States)

Santello, Marco; Bianchi, Matteo; Gabiccini, Marco; Ricciardi, Emiliano; Salvietti, Gionata; Prattichizzo, Domenico; Ernst, Marc; Moscatelli, Alessandro; Jorntell, Henrik; Kappers, Astrid M. L.; Kyriakopoulos, Kostas; Schaeffer, Alin Abu; Castellini, Claudio; Bicchi, Antonio

2016-07-01

We would like to thank all commentators for their insightful commentaries. Thanks to their diverse and complementary expertise in neuroscience and robotics, the commentators have provided us with the opportunity to further discuss state-of-the-art and gaps in the integration of neuroscience and robotics reviewed in our article. We organized our reply in two sections that capture the main points of all commentaries [1-9]: (1) Advantages and limitations of the synergy approach in neuroscience and robotics, and (2) Learning and role of sensory feedback in biological and robotics synergies.
An Infant Development-inspired Approach to Robot Hand-eye Coordination

Directory of Open Access Journals (Sweden)

Fei Chao

2014-02-01

Full Text Available This paper presents a novel developmental learning approach for hand-eye coordination in an autonomous robotic system. Robotic hand-eye coordination plays an important role in dealing with real-time environments. Under the approach, infant developmental patterns are introduced to build our robot's learning system. The method works by first constructing a brain-like computational structure to control the robot, and then by using infant behavioural patterns to build a hand-eye coordination learning algorithm. This work is supported by an experimental evaluation, which shows that the control system is implemented simply, and that the learning approach provides fast and incremental learning of behavioural competence.
Learning to locate an odour source with a mobile robot

OpenAIRE

Duckett, T.; Axelsson, M.; Saffiotti, A.

2001-01-01

We address the problem of enabling a mobile robot to locate a stationary odour source using an electronic nose constructed from gas sensors. On the hardware side, we use a stereo nose architecture consisting of two parallel chambers, each containing an identical set of sensors. On the software side, we use a recurrent artificial neural network to learn the direction to a stationary source from a time series of sensor readings. This contrasts with previous approaches, that rely on the existenc...
Hoe leer je iemand effectief te leren?

NARCIS (Netherlands)

Kester, Liesbeth; Koper, Rob; Gijselaers, Jérôme; Bahreini, Kiavash; De Vries, Fred; Wetzels, Sandra; Kirschner, Paul A.; Berkhout, Jeroen; Storm, Jeroen

2012-01-01

Kester, L., Koper, R., Gijselaers, J., Bahreini, K., De Vries, F., Berkhout, J., & Storm, J. (2012, 30 maart). Hoe leer je iemand effectief te leren? Masterclass in de OpenU community. Open universiteit, Heerlen, Nederland. Beschikbaar op
Evaluation of linearly solvable Markov decision process with dynamic model learning in a mobile robot navigation task.

Science.gov (United States)

Kinjo, Ken; Uchibe, Eiji; Doya, Kenji

2013-01-01

Linearly solvable Markov Decision Process (LMDP) is a class of optimal control problem in which the Bellman's equation can be converted into a linear equation by an exponential transformation of the state value function (Todorov, 2009b). In an LMDP, the optimal value function and the corresponding control policy are obtained by solving an eigenvalue problem in a discrete state space or an eigenfunction problem in a continuous state using the knowledge of the system dynamics and the action, state, and terminal cost functions. In this study, we evaluate the effectiveness of the LMDP framework in real robot control, in which the dynamics of the body and the environment have to be learned from experience. We first perform a simulation study of a pole swing-up task to evaluate the effect of the accuracy of the learned dynamics model on the derived the action policy. The result shows that a crude linear approximation of the non-linear dynamics can still allow solution of the task, despite with a higher total cost. We then perform real robot experiments of a battery-catching task using our Spring Dog mobile robot platform. The state is given by the position and the size of a battery in its camera view and two neck joint angles. The action is the velocities of two wheels, while the neck joints were controlled by a visual servo controller. We test linear and bilinear dynamic models in tasks with quadratic and Guassian state cost functions. In the quadratic cost task, the LMDP controller derived from a learned linear dynamics model performed equivalently with the optimal linear quadratic regulator (LQR). In the non-quadratic task, the LMDP controller with a linear dynamics model showed the best performance. The results demonstrate the usefulness of the LMDP framework in real robot control even when simple linear models are used for dynamics learning.
Unibot, a Universal Agent Architecture for Robots

Directory of Open Access Journals (Sweden)

Saša Mladenović

2017-01-01

Full Text Available Today there are numerous robots in different applications domains despite the fact that they still have limitations in perception, actuation and decision process. Consequently, robots usually have limited autonomy, they are domain specific or have difficulty to adapt on new environments. Learning is the property that makes an agent intelligent and the crucial property that a robot should have to proliferate into the human society. Embedding the learning ability into the robot may simplify the development of the robot control mechanism. The motivation for this research is to develop the agent architecture of the universal robot – Unibot. In our approach the agent is the robot i.e. Unibot that acts in the physical world and is capable of learning. The Unibot conducts several simultaneous simulations of a problem of interest like path-finding. The novelty in our approach is the Multi-Agent Decision Support System which is developed and integrated into the Unibot agent architecture in order to execute simultaneous simulations. Furthermore, the Unibot calculates and evaluates between multiple solutions, decides which action should be performed and performs the action. The prototype of the Unibot agent architecture is described and evaluated in the experiment supported by the Lego Mindstorms robot and the NetLogo.
Robotic Motion Learning Framework to Promote Social Engagement

Directory of Open Access Journals (Sweden)

Rachael Burns

2018-02-01

Full Text Available Imitation is a powerful component of communication between people, and it poses an important implication in improving the quality of interaction in the field of human–robot interaction (HRI. This paper discusses a novel framework designed to improve human–robot interaction through robotic imitation of a participant’s gestures. In our experiment, a humanoid robotic agent socializes with and plays games with a participant. For the experimental group, the robot additionally imitates one of the participant’s novel gestures during a play session. We hypothesize that the robot’s use of imitation will increase the participant’s openness towards engaging with the robot. Experimental results from a user study of 12 subjects show that post-imitation, experimental subjects displayed a more positive emotional state, had higher instances of mood contagion towards the robot, and interpreted the robot to have a higher level of autonomy than their control group counterparts did. These results point to an increased participant interest in engagement fueled by personalized imitation during interaction.
Reinforcement Learning on autonomous humanoid robots

NARCIS (Netherlands)

Schuitema, E.

2012-01-01

Service robots have the potential to be of great value in households, health care and other labor intensive environments. However, these environments are typically unique, not very structured and frequently changing, which makes it difficult to make service robots robust and versatile through manual
Robotic Mission to Mars: Hands-on, minds-on, web-based learning

Science.gov (United States)

Mathers, Naomi; Goktogen, Ali; Rankin, John; Anderson, Marion

2012-11-01

Problem-based learning has been demonstrated as an effective methodology for developing analytical skills and critical thinking. The use of scenario-based learning incorporates problem-based learning whilst encouraging students to collaborate with their colleagues and dynamically adapt to their environment. This increased interaction stimulates a deeper understanding and the generation of new knowledge. The Victorian Space Science Education Centre (VSSEC) uses scenario-based learning in its Mission to Mars, Mission to the Orbiting Space Laboratory and Primary Expedition to the M.A.R.S. Base programs. These programs utilize methodologies such as hands-on applications, immersive-learning, integrated technologies, critical thinking and mentoring to engage students in Science, Technology, Engineering and Mathematics (STEM) and highlight potential career paths in science and engineering. The immersive nature of the programs demands specialist environments such as a simulated Mars environment, Mission Control and Space Laboratory, thus restricting these programs to a physical location and limiting student access to the programs. To move beyond these limitations, VSSEC worked with its university partners to develop a web-based mission that delivered the benefits of scenario-based learning within a school environment. The Robotic Mission to Mars allows students to remotely control a real rover, developed by the Australian Centre for Field Robotics (ACFR), on the VSSEC Mars surface. After completing a pre-mission training program and site selection activity, students take on the roles of scientists and engineers in Mission Control to complete a mission and collect data for further analysis. Mission Control is established using software developed by the ACRI Games Technology Lab at La Trobe University using the principles of serious gaming. The software allows students to control the rover, monitor its systems and collect scientific data for analysis. This program encourages
Are Commercial "Personal Robots" Ready for Language Learning? Focus on Second Language Speech

Science.gov (United States)

Moussalli, Souheila; Cardoso, Walcir

2016-01-01

Today's language classrooms are challenged with limited classroom time and lack of input, and output practice in a stress-free environment (Hsu, 2015). The use of commercial, readily available tools such as Personal Robots (PRs; e.g. Amazon's Echo, Jibo) might promote language learning by freeing up class time, allowing for a more focused…
Reasons for singularity in robot teleoperation

DEFF Research Database (Denmark)

Marhenke, Ilka; Fischer, Kerstin; Savarimuthu, Thiusius Rajeeth

2014-01-01

In this paper, the causes for singularity of a robot arm in teleoperation for robot learning from demonstration are analyzed. Singularity is the alignment of robot joints, which prevents the configuration of the inverse kinematics. Inspired by users' own hypotheses, we investigated speed and dela...
Going Green Robots

Science.gov (United States)

Nelson, Jacqueline M.

2011-01-01

In looking at the interesting shapes and sizes of old computer parts, creating robots quickly came to the author's mind. In this article, she describes how computer parts can be used creatively. Students will surely enjoy creating their very own robots while learning about the importance of recycling in the society. (Contains 1 online resource.)
Measuring and Modelling Delays in Robot Manipulators for Temporally Precise Control using Machine Learning

DEFF Research Database (Denmark)

Andersen, Thomas Timm; Amor, Heni Ben; Andersen, Nils Axel

2015-01-01

and separate. In this paper, we present a data-driven methodology for separating and modelling inherent delays during robot control. We show how both actuation and response delays can be modelled using modern machine learning methods. The resulting models can be used to predict the delays as well...
Novelty Detection for Interactive Pose Recognition by a Social Robot

Directory of Open Access Journals (Sweden)

Victor Gonzalez-Pacheco

2015-04-01

Full Text Available Active robot learners take an active role in their own learning by making queries to their human teachers when they receive new data. However, not every received input is useful for the robot, and asking for non-informative inputs or asking too many questions might worsen the user's perception of the robot. We present a novelty detection system that enables a robot to ask labels for new stimuli only when they seem both novel and interesting. Our system separates the decision process into two steps: first, it discriminates novel from known stimuli, and second, it estimates if these stimuli are likely to happen again. Our approach uses the notion of curiosity, which controls the eagerness with which the robot asks questions to the user. We evaluate our approach in the domain of pose learning by training our robot with a set of pointing poses able to detect up to 84%, 79%, and 78% of the observed novelties in three different experiments. Our approach enables robots to keep learning continuously, even after training is finished. The introduction of the curiosity parameter allows tuning, for the conditions in which the robot should want to learn more.
Self-Imitation and Environmental Scaffolding for Robot Teaching

Directory of Open Access Journals (Sweden)

Joe Saunders

2007-03-01

Full Text Available Imitative learning and learning by observation are social mechanisms that allow a robot to acquire knowledge from a human or another robot. However to be able to obtain skills in this way the robot faces many complex issues, one of which is that of finding solutions to the correspondence problem. Evolutionary predecessors to observational imitation may have been self-imitation where an agent avoids the complexities of the correspondence problem by learning and replicating actions it has experienced through the manipulation of its body. We investigate how a robotic control and teaching system using self-imitation can be constructed with reference to psychological models of motor control and ideas from social scaffolding seen in animals. Within these scaffolded environments sets of competencies can be built by constructing hierarchical state/action memory maps of the robot's interaction within that environment. The scaffolding process provides a mechanism to enable learning to be scaled up. The resulting system allows a human trainer to teach a robot new skills and modify skills that the robot may possess. Additionally the system allows the robot to notify the trainer when it is being taught skills it already has in its repertoire and to direct and focus its attention and sensor resources to relevant parts of the skill being executed. We argue that these mechanisms may be a first step towards the transformation from self-imitation to observational imitation. The system is validated on a physical pioneer robot that is taught using self-imitation to track, follow and point to a patterned object.
Self-imitation and Environmental Scaffolding for Robot Teaching

Directory of Open Access Journals (Sweden)

Chrystopher L. Nehaniv

2008-11-01

Full Text Available Imitative learning and learning by observation are social mechanisms that allow a robot to acquire knowledge from a human or another robot. However to be able to obtain skills in this way the robot faces many complex issues, one of which is that of finding solutions to the correspondence problem. Evolutionary predecessors to observational imitation may have been self-imitation where an agent avoids the complexities of the correspondence problem by learning and replicating actions it has experienced through the manipulation of its body. We investigate how a robotic control and teaching system using self-imitation can be constructed with reference to psychological models of motor control and ideas from social scaffolding seen in animals. Within these scaffolded environments sets of competencies can be built by constructing hierarchical state/action memory maps of the robot's interaction within that environment. The scaffolding process provides a mechanism to enable learning to be scaled up. The resulting system allows a human trainer to teach a robot new skills and modify skills that the robot may possess. Additionally the system allows the robot to notify the trainer when it is being taught skills it already has in its repertoire and to direct and focus its attention and sensor resources to relevant parts of the skill being executed. We argue that these mechanisms may be a first step towards the transformation from self-imitation to observational imitation. The system is validated on a physical pioneer robot that is taught using self-imitation to track, follow and point to a patterned object.

A Game Theoretic Approach to Swarm Robotics

Directory of Open Access Journals (Sweden)

S. N. Givigi

2006-01-01

Full Text Available In this article, we discuss some techniques for achieving swarm intelligent robots through the use of traits of personality. Traits of personality are characteristics of each robot that, altogether, define the robot's behaviours. We discuss the use of evolutionary psychology to select a set of traits of personality that will evolve due to a learning process based on reinforcement learning. The use of Game Theory is introduced, and some simulations showing its potential are reported.
Kaj je guoxue? – vzorci organizacije znanja na primeru dveh »slovarjev« (2009 in 2014

Directory of Open Access Journals (Sweden)

Raoul David Findeisen

2016-05-01

Full Text Available Prispevek obravnava novejše razvoje ali, bolje rečeno, preporod koncepta guoxue, ki je nastal pred dobrim stoletjem. Gre za reaktiviran, če ne celo reakcionaren koncept, ki ga v indoevropske jezike večinoma prevajajo kot »kitajsko znanje«, »avtohtono znanje«, »nacionalni nauki« ipd. V njem lahko vidimo neke vrste obrambno reakcijo v smislu paradigmatskega preobrata, ki je pogojen z naraščajočo prevlado (Zahodne znanosti in tehnologije, ki se je pričela v poznem 19. stoletju in je ponovno vse bolj opazna v sodobni Kitajski. Guoxue je pravzaprav poskus legitimacije vsega, kar lahko povzamemo s frazo »tradicionalnih« naukov in znanj, ki so večinoma dojeti kot nekaj »drugačnega« in nekaj, kar je v nasprotju z vseobsežno dominacijo superiornosti znanosti in tehnologije. A vendar ni nikjer mogoče najti natančnih definicij tega pojma. Spiski in zbirke vseh mogočih diskurzov, ki naj bi predstavljali del guoxue, ponudijo kvečjemu implicitne definicije, ki jih je posledično mogoče samo naknadno obdelati. Izjema so samo tisti primerki guoxue, pri katerih se lahko Kitajska pohvali z nedvoumno in očitno prednostjo pred Zahodom.
Design of Smart Educational Robot as a Tool For Teaching Media Based on Contextual Teaching and Learning to Improve the Skill of Electrical Engineering Student

Science.gov (United States)

Zuhrie, M. S.; Basuki, I.; Asto, B. I. G. P.; Anifah, L.

2018-04-01

The development of robotics in Indonesia has been very encouraging. The barometer is the success of the Indonesian Robot Contest. The focus of research is a teaching module manufacturing, planning mechanical design, control system through microprocessor technology and maneuverability of the robot. Contextual Teaching and Learning (CTL) strategy is the concept of learning where the teacher brings the real world into the classroom and encourage students to make connections between knowledge possessed by its application in everyday life. This research the development model used is the 4-D model. This Model consists of four stages: Define Stage, Design Stage, Develop Stage, and Disseminate Stage. This research was conducted by applying the research design development with the aim to produce a tool of learning in the form of smart educational robot modules and kit based on Contextual Teaching and Learning at the Department of Electrical Engineering to improve the skills of the Electrical Engineering student. Socialization questionnaires showed that levels of the student majoring in electrical engineering competencies image currently only limited to conventional machines. The average assessment is 3.34 validator included in either category. Modules developed can give hope to the future are able to produce Intelligent Robot Tool for Teaching.
An Adaptive Robot Game

DEFF Research Database (Denmark)

Hansen, Søren Tranberg; Svenstrup, Mikael; Dalgaard, Lars

2010-01-01

The goal of this paper is to describe an adaptive robot game, which motivates elderly people to do a regular amount of physical exercise while playing. One of the advantages of robot based games is that the initiative to play can be taken autonomously by the robot. In this case, the goal is to im......The goal of this paper is to describe an adaptive robot game, which motivates elderly people to do a regular amount of physical exercise while playing. One of the advantages of robot based games is that the initiative to play can be taken autonomously by the robot. In this case, the goal...... is to improve the mental and physical state of the user by playing a physical game with the robot. Ideally, a robot game should be simple to learn but difficult to master, providing an appropriate degree of challenge for players with different skills. In order to achieve that, the robot should be able to adapt...
Adaptive and Energy Efficient Walking in a Hexapod Robot under Neuromechanical Control and Sensorimotor Learning

DEFF Research Database (Denmark)

Xiong, Xiaofeng; Wörgötter, Florentin; Manoonpong, Poramate

2016-01-01

The control of multilegged animal walking is a neuromechanical process, and to achieve this in an adaptive and energy efficient way is a difficult and challenging problem. This is due to the fact that this process needs in real time: 1) to coordinate very many degrees of freedom of jointed legs; 2......) to generate the proper leg stiffness (i.e., compliance); and 3) to determine joint angles that give rise to particular positions at the endpoints of the legs. To tackle this problem for a robotic application, here we present a neuromechanical controller coupled with sensorimotor learning. The controller...... energy efficient walking, compared to other small legged robots. In addition, this paper also shows that the tight combination of neural control with tunable muscle-like functions, guided by sensory feedback and coupled with sensorimotor learning, is a way forward to better understand and solve adaptive...
"Kar se Janezek nauči…", je premalo

Directory of Open Access Journals (Sweden)

Maca Jogan

2009-12-01

Full Text Available Vprašanje družbene neenakosti žensk in moških je v drugi polovici 20. stoletja postalo svetovni problem, ki so se ga resno lotile pomembne mednarodne organizacije (OZN in državne skupnosti (EU ter posamezne države. Zagotavljanje enakih možnosti za oba spola na vseh področjih in na vseh ravneh življenja je postalo ena prednostnih nalog EU, kajti v sodobnih družbah še obstajajo različne oblike (odkrite in zlasti prikrite diskriminacije po spolu. Avtorica v prvem delu razkriva ključne značilnosti moškosrediščnega tradicionalnega družbenega reda v zahodni civilizaciji ter predstavlja glavne značilnosti in nosilce zagotavljanja enakih možnosti žensk in moških od globalne do evropske ravni. Pomembno vlogo pri odpravljanju diskriminacije imata formalno in neformalno izobraževanje. Ker pa morajo biti tudi izobraževalci in nosilci novih vzorcev odnosov med spoloma izobraženi, je pomembno vseživljenjsko izobraževanje odraslih, zlasti tistih, ki zasedajo ključne položaje odločanja na vseh področjih življenja. Zato avtorica v drugem delu prispevka predstavlja nekatere pristope in prakse takšnega izobraževanja v drugih državah članicah EU ter v Sloveniji.
Teaching Human Poses Interactively to a Social Robot

Science.gov (United States)

Gonzalez-Pacheco, Victor; Malfaz, Maria; Fernandez, Fernando; Salichs, Miguel A.

2013-01-01

The main activity of social robots is to interact with people. In order to do that, the robot must be able to understand what the user is saying or doing. Typically, this capability consists of pre-programmed behaviors or is acquired through controlled learning processes, which are executed before the social interaction begins. This paper presents a software architecture that enables a robot to learn poses in a similar way as people do. That is, hearing its teacher's explanations and acquiring new knowledge in real time. The architecture leans on two main components: an RGB-D (Red-, Green-, Blue- Depth) -based visual system, which gathers the user examples, and an Automatic Speech Recognition (ASR) system, which processes the speech describing those examples. The robot is able to naturally learn the poses the teacher is showing to it by maintaining a natural interaction with the teacher. We evaluate our system with 24 users who teach the robot a predetermined set of poses. The experimental results show that, with a few training examples, the system reaches high accuracy and robustness. This method shows how to combine data from the visual and auditory systems for the acquisition of new knowledge in a natural manner. Such a natural way of training enables robots to learn from users, even if they are not experts in robotics. PMID:24048336
Teaching Human Poses Interactively to a Social Robot

Directory of Open Access Journals (Sweden)

Miguel A. Salichs

2013-09-01

Full Text Available The main activity of social robots is to interact with people. In order to do that, the robot must be able to understand what the user is saying or doing. Typically, this capability consists of pre-programmed behaviors or is acquired through controlled learning processes, which are executed before the social interaction begins. This paper presents a software architecture that enables a robot to learn poses in a similar way as people do. That is, hearing its teacher’s explanations and acquiring new knowledge in real time. The architecture leans on two main components: an RGB-D (Red-, Green-, Blue- Depth -based visual system, which gathers the user examples, and an Automatic Speech Recognition (ASR system, which processes the speech describing those examples. The robot is able to naturally learn the poses the teacher is showing to it by maintaining a natural interaction with the teacher. We evaluate our system with 24 users who teach the robot a predetermined set of poses. The experimental results show that, with a few training examples, the system reaches high accuracy and robustness. This method shows how to combine data from the visual and auditory systems for the acquisition of new knowledge in a natural manner. Such a natural way of training enables robots to learn from users, even if they are not experts in robotics.
Control algorithms for autonomous robot navigation

International Nuclear Information System (INIS)

Jorgensen, C.C.

1985-01-01

This paper examines control algorithm requirements for autonomous robot navigation outside laboratory environments. Three aspects of navigation are considered: navigation control in explored terrain, environment interactions with robot sensors, and navigation control in unanticipated situations. Major navigation methods are presented and relevance of traditional human learning theory is discussed. A new navigation technique linking graph theory and incidental learning is introduced
A neural learning classifier system with self-adaptive constructivism for mobile robot control.

Science.gov (United States)

Hurst, Jacob; Bull, Larry

2006-01-01

For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.
Biologically-Inspired Adaptive Obstacle Negotiation Behavior of Hexapod Robots

DEFF Research Database (Denmark)

Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

2014-01-01

by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots. It combines locomotion control, backbone joint control, local leg reflexes, and neural learning. While the first three components generate locomotion including walking and climbing, the neural...... learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, e.g., variable obstacle heights and different walking gaits. By successfully learning the association of an early, predictive signal (conditioned stimulus, CS) and a late, reflex signal...... (unconditioned stimulus, UCS), both provided by ultrasonic sensors at the front of the robot, the robot can autonomously find an appropriate distance from an obstacle to initiate climbing. The adaptive neural control was developed and tested first on a physical robot simulation, and was then successfully...
An e-Learning System with MR for Experiments Involving Circuit Construction to Control a Robot

Science.gov (United States)

Takemura, Atsushi

2016-01-01

This paper proposes a novel e-Learning system for technological experiments involving electronic circuit-construction and controlling robot motion that are necessary in the field of technology. The proposed system performs automated recognition of circuit images transmitted from individual learners and automatically supplies the learner with…
A psychology based approach for longitudinal development in cognitive robotics

Directory of Open Access Journals (Sweden)

James eLaw

2014-01-01

Full Text Available A major challenge in robotics is the ability to learn, from novel experiences, new behaviour that is useful for achieving new goals and skills. Autonomous systems must be able to learn solely through the environment, thus ruling out a priori task knowledge, tuning, extensive training, or other forms of pre-programming. Learning must also be cumulative and incremental, as complex skills are built on top of primitive skills. Additionally, it must be driven by intrinsic motivation because formative experience is gained through autonomous activity, even in the absence of extrinsic goals or tasks. This paper presents an approach to these issues through robotic implementations inspired by the learning behaviour of human infants. We describe an approach to developmental learning and present results from a demonstration of longitudinal development on an iCub humanoid robot. The results cover the rapid emergence of staged behaviour, the role of constraints in development, the effect of bootstrapping between stages, and the use of a schema memory of experiential fragments in learning new skills. The context is a longitudinalexperiment in which the robot advanced from uncontrolled motor babbling to skilled hand/eyeintegrated reaching and basic manipulation of objects. This approach offers promise for furtherfast and effective sensory-motor learning techniques for robotic learning.
Examining Students' Proportional Reasoning Strategy Levels as Evidence of the Impact of an Integrated LEGO Robotics and Mathematics Learning Experience

Science.gov (United States)

Martínez Ortiz, Araceli

2015-01-01

The presented study used a problem-solving experience in engineering design with LEGO robotics materials as the real-world mathematics-learning context. The goals of the study were (a) to determine if a short but intensive extracurricular learning experience would lead to significant student learning of a particular academic topic and (b) to…
Habituation: a non-associative learning rule design for spiking neurons and an autonomous mobile robots implementation

International Nuclear Information System (INIS)

Cyr, André; Boukadoum, Mounir

2013-01-01

This paper presents a novel bio-inspired habituation function for robots under control by an artificial spiking neural network. This non-associative learning rule is modelled at the synaptic level and validated through robotic behaviours in reaction to different stimuli patterns in a dynamical virtual 3D world. Habituation is minimally represented to show an attenuated response after exposure to and perception of persistent external stimuli. Based on current neurosciences research, the originality of this rule includes modulated response to variable frequencies of the captured stimuli. Filtering out repetitive data from the natural habituation mechanism has been demonstrated to be a key factor in the attention phenomenon, and inserting such a rule operating at multiple temporal dimensions of stimuli increases a robot's adaptive behaviours by ignoring broader contextual irrelevant information. (paper)
Habituation: a non-associative learning rule design for spiking neurons and an autonomous mobile robots implementation.

Science.gov (United States)

Cyr, André; Boukadoum, Mounir

2013-03-01

This paper presents a novel bio-inspired habituation function for robots under control by an artificial spiking neural network. This non-associative learning rule is modelled at the synaptic level and validated through robotic behaviours in reaction to different stimuli patterns in a dynamical virtual 3D world. Habituation is minimally represented to show an attenuated response after exposure to and perception of persistent external stimuli. Based on current neurosciences research, the originality of this rule includes modulated response to variable frequencies of the captured stimuli. Filtering out repetitive data from the natural habituation mechanism has been demonstrated to be a key factor in the attention phenomenon, and inserting such a rule operating at multiple temporal dimensions of stimuli increases a robot's adaptive behaviours by ignoring broader contextual irrelevant information.
Can Social Learning Increase Learning Speed, Performance or Both?

NARCIS (Netherlands)

Heinerman, J.V.; Stork, J.; Rebolledo Coy, M.A.; Hubert, J.G.; Eiben, A.E.; Bartz-Beielstein, Thomas; Haasdijk, Evert

2017-01-01

Social learning enables multiple robots to share learned experiences while completing a task. The literature offers contradicting examples of its benefits; robots trained with social learning reach a higher performance, an increased learning speed, or both, compared to their individual learning
Co je důležitější v krajině: estetika nebo biodiverzita?

Directory of Open Access Journals (Sweden)

Mojmír Vlašín

2014-10-01

Full Text Available Uvažujeme-li o hodnotách jako je estetika a biodiverzita musíme především definovat, z jakého zorného úhlu chceme tyto fenomény porovnávat. Pokud se postavíme na pozice biocentrického světonázoru (A. Schweitzer, A. Ness, J. Lovelock pak je otázka uvedená v názvu příspěvku bezesmyslu. Je totiž zřejmé, že život, příroda či biodiverzita je hodnotnou sama o sobě a estetika je mimo hru. Pokud se postavíme na pozice antropocentrického světonázoru, je možné krajinu vnímat jako životní prostor pro člověka a tedy jako (1 zdroj potravy (2 zdroj „rostných“ a nerostných surovin,(3 místo pro bydlení nebo (4 místo pro rekreaci. Pak je tedy možné postavit otázku, co očekáváme od krajiny, které funkce by měla plnit především. S ohledem na odpověď je pak třeba analyzovat míru významnosti estetiky (krásy a či biodiverzity (přírodní rozmanitosti pro krajinu.
CANINE: a robotic mine dog

Science.gov (United States)

Stancil, Brian A.; Hyams, Jeffrey; Shelley, Jordan; Babu, Kartik; Badino, Hernán.; Bansal, Aayush; Huber, Daniel; Batavia, Parag

2013-01-01

Neya Systems, LLC competed in the CANINE program sponsored by the U.S. Army Tank Automotive Research Development and Engineering Center (TARDEC) which culminated in a competition held at Fort Benning as part of the 2012 Robotics Rodeo. As part of this program, we developed a robot with the capability to learn and recognize the appearance of target objects, conduct an area search amid distractor objects and obstacles, and relocate the target object in the same way that Mine dogs and Sentry dogs are used within military contexts for exploration and threat detection. Neya teamed with the Robotics Institute at Carnegie Mellon University to develop vision-based solutions for probabilistic target learning and recognition. In addition, we used a Mission Planning and Management System (MPMS) to orchestrate complex search and retrieval tasks using a general set of modular autonomous services relating to robot mobility, perception and grasping.
Embedded System and Robotic Education in a Blended Learning Environment Utilizing Remote and Virtual Labs in the Cloud, Accompanied by ‘Robotic HomeLab Kit’

Directory of Open Access Journals (Sweden)

Sven Seiler

2012-12-01

Full Text Available It is impossible to imagine everyday life without embedded devices and robotic applications, as they are utilized in almost every nowadays technical product. And there is a frantic need of well-educated developers, designers and programmers to handle and further evolve this existing technology. The domain itself is in a big change because the borders of pure ICT and embedded system are fusing and according to this process new methods for teaching these disciplines are necessary. It is important that ICT education will become more and more to real systems education, instead of just computer software programming, but in most curricula these two domains are still separated. The paper addresses a novel and implemented solution for teaching and learning of Robotics and embedded systems, while setting in remote labs and modern Internet technology into overall learning process. The proposed concept builds the bridge for a simple and logical study process by utilizing ICT for controlling and understanding real word processes and situations. The introduced blended learning concept covers several educational levels, starting from first and second level education up to university education and life-long learning. The solution is covered with hands-on mobile hardware kits, collaborative e-tools and remote labs. The focus in this paper is on the integration of the overall concept and an evaluation of the given courses.

Prototyping and Simulation of Robot Group Intelligence using Kohonen Networks.

Science.gov (United States)

Wang, Zhijun; Mirdamadi, Reza; Wang, Qing

2016-01-01

Intelligent agents such as robots can form ad hoc networks and replace human being in many dangerous scenarios such as a complicated disaster relief site. This project prototypes and builds a computer simulator to simulate robot kinetics, unsupervised learning using Kohonen networks, as well as group intelligence when an ad hoc network is formed. Each robot is modeled using an object with a simple set of attributes and methods that define its internal states and possible actions it may take under certain circumstances. As the result, simple, reliable, and affordable robots can be deployed to form the network. The simulator simulates a group of robots as an unsupervised learning unit and tests the learning results under scenarios with different complexities. The simulation results show that a group of robots could demonstrate highly collaborative behavior on a complex terrain. This study could potentially provide a software simulation platform for testing individual and group capability of robots before the design process and manufacturing of robots. Therefore, results of the project have the potential to reduce the cost and improve the efficiency of robot design and building.
Robotovo pojasnjevanje svojih odločitev

OpenAIRE

Cvetkov, Martin

2017-01-01

Eden izmed ciljev umetne intelegence je razvoj robotov, ki so sposobni ustvarjati načrte in delovati samostojno, kot ljudje. Cilj tega magistrskega dela je zagotoviti robotsko roko s šestimi prostostnimi stopnjami, ki je sposobna manipulirati s predmeti z uporabo inverzne kinematike ter samostojno planirati reševanje danih nalog. Hkrati, med izvajanjem načrta, robot pojasni vsako svojo odločitev oz. akcijo, ki jo izvede. Motiv za to magistrsko delo je to, da pogosto vidimo, kako robot...
Analysis of Lamarckian Evolution in Morphologically Evolving Robots

NARCIS (Netherlands)

Jelisavcic, Milan; Kiesel, Rafael; Glette, Kyrre; Haasdijk, Evert; Eiben, A.E.

Evolving robot morphologies implies the need for lifetime learning so that newborn robots can learn to manipulate their bodies. An individual’s morphology will obviously combine traits of all its parents; it must adapt its own controller to suit its morphology, and cannot rely on the controller of
"Alarm-corrected" ergonomic armrest use could improve learning curves of novices on robotic simulator.

Science.gov (United States)

Yang, Kun; Perez, Manuela; Hossu, Gabriela; Hubert, Nicolas; Perrenot, Cyril; Hubert, Jacques

2017-01-01

In robotic surgery, the professional ergonomic habit of using an armrest reduces operator fatigue and increases the precision of motion. We designed and validated a pressure surveillance system (PSS) based on force sensors to investigate armrest use. The objective was to evaluate whether adding an alarm to the PSS system could shorten ergonomic training and improve performance. Twenty robot and simulator-naïve participants were recruited and randomized in two groups (A and B). The PSS was installed on a robotic simulator, the dV-Trainer, to detect contact with the armrest. The Group A members completed three tasks on the dV-Trainer without the alarm, making 15 attempts at each task. The Group B members practiced the first two tasks with the alarm and then completed the final tasks without the alarm. The simulator provided an overall score reflecting the trainees' performance. We used the new concept of an "armrest load" score to describe the ergonomic habit of using the armrest. Group B had a significantly higher performance score (p ergonomic errors and accelerated professional ergonomic habit acquisition. The combination of the PSS and alarm is effective in significantly shortening the learning curve in the robotic training process.
SU-G-JeP3-03: Effect of Robot Pose On Beam Blocking for Ultrasound Guided SBRT of the Prostate

Energy Technology Data Exchange (ETDEWEB)

Gerlach, S; Schlaefer, A [Hamburg University of Technology, Hamburg (Germany); Kuhlemann, I; Ernst, F [Universitaet zu Luebeck, Luebeck (Germany); Fuerweger, C [European Cyberknife Center Munich, Munich (Germany)

2016-06-15

Purpose: Ultrasound presents a fast, volumetric image modality for real-time tracking of abdominal organ motion. How-ever, ultrasound transducer placement during radiation therapy is challenging. Recently, approaches using robotic arms for intra-treatment ultrasound imaging have been proposed. Good and reliable imaging requires placing the transducer close to the PTV. We studied the effect of a seven degrees of freedom robot on the fea-sible beam directions. Methods: For five CyberKnife prostate treatment plans we established viewports for the transducer, i.e., points on the patient surface with a soft tissue view towards the PTV. Choosing a feasible transducer pose and using the kinematic redundancy of the KUKA LBR iiwa robot, we considered three robot poses. Poses 1 to 3 had the elbow point anterior, superior, and inferior, respectively. For each pose and each beam starting point, the pro-jections of robot and PTV were computed. We added a 20 mm margin accounting for organ / beam motion. The number of nodes for which the PTV was partially of fully blocked were established. Moreover, the cumula-tive overlap for each of the poses and the minimum overlap over all poses were computed. Results: The fully and partially blocked nodes ranged from 12% to 20% and 13% to 27%, respectively. Typically, pose 3 caused the fewest blocked nodes. The cumulative overlap ranged from 19% to 29%. Taking the minimum overlap, i.e., considering moving the robot’s elbow while maintaining the transducer pose, the cumulative over-lap was reduced to 16% to 18% and was 3% to 6% lower than for the best individual pose. Conclusion: Our results indicate that it is possible to identify feasible ultrasound transducer poses and to use the kinematic redundancy of a 7 DOF robot to minimize the impact of the imaging subsystem on the feasible beam directions for ultrasound guided and motion compensated SBRT. Research partially funded by DFG grants ER 817/1-1 and SCHL 1844/3-1.
SU-G-JeP3-03: Effect of Robot Pose On Beam Blocking for Ultrasound Guided SBRT of the Prostate

International Nuclear Information System (INIS)

Gerlach, S; Schlaefer, A; Kuhlemann, I; Ernst, F; Fuerweger, C

2016-01-01

Purpose: Ultrasound presents a fast, volumetric image modality for real-time tracking of abdominal organ motion. How-ever, ultrasound transducer placement during radiation therapy is challenging. Recently, approaches using robotic arms for intra-treatment ultrasound imaging have been proposed. Good and reliable imaging requires placing the transducer close to the PTV. We studied the effect of a seven degrees of freedom robot on the fea-sible beam directions. Methods: For five CyberKnife prostate treatment plans we established viewports for the transducer, i.e., points on the patient surface with a soft tissue view towards the PTV. Choosing a feasible transducer pose and using the kinematic redundancy of the KUKA LBR iiwa robot, we considered three robot poses. Poses 1 to 3 had the elbow point anterior, superior, and inferior, respectively. For each pose and each beam starting point, the pro-jections of robot and PTV were computed. We added a 20 mm margin accounting for organ / beam motion. The number of nodes for which the PTV was partially of fully blocked were established. Moreover, the cumula-tive overlap for each of the poses and the minimum overlap over all poses were computed. Results: The fully and partially blocked nodes ranged from 12% to 20% and 13% to 27%, respectively. Typically, pose 3 caused the fewest blocked nodes. The cumulative overlap ranged from 19% to 29%. Taking the minimum overlap, i.e., considering moving the robot’s elbow while maintaining the transducer pose, the cumulative over-lap was reduced to 16% to 18% and was 3% to 6% lower than for the best individual pose. Conclusion: Our results indicate that it is possible to identify feasible ultrasound transducer poses and to use the kinematic redundancy of a 7 DOF robot to minimize the impact of the imaging subsystem on the feasible beam directions for ultrasound guided and motion compensated SBRT. Research partially funded by DFG grants ER 817/1-1 and SCHL 1844/3-1.
Intelligence for Human-Assistant Planetary Surface Robots

Science.gov (United States)

Hirsh, Robert; Graham, Jeffrey; Tyree, Kimberly; Sierhuis, Maarten; Clancey, William J.

2006-01-01

The central premise in developing effective human-assistant planetary surface robots is that robotic intelligence is needed. The exact type, method, forms and/or quantity of intelligence is an open issue being explored on the ERA project, as well as others. In addition to field testing, theoretical research into this area can help provide answers on how to design future planetary robots. Many fundamental intelligence issues are discussed by Murphy [2], including (a) learning, (b) planning, (c) reasoning, (d) problem solving, (e) knowledge representation, and (f) computer vision (stereo tracking, gestures). The new "social interaction/emotional" form of intelligence that some consider critical to Human Robot Interaction (HRI) can also be addressed by human assistant planetary surface robots, as human operators feel more comfortable working with a robot when the robot is verbally (or even physically) interacting with them. Arkin [3] and Murphy are both proponents of the hybrid deliberative-reasoning/reactive-execution architecture as the best general architecture for fully realizing robot potential, and the robots discussed herein implement a design continuously progressing toward this hybrid philosophy. The remainder of this chapter will describe the challenges associated with robotic assistance to astronauts, our general research approach, the intelligence incorporated into our robots, and the results and lessons learned from over six years of testing human-assistant mobile robots in field settings relevant to planetary exploration. The chapter concludes with some key considerations for future work in this area.
Je, a versatile suite to handle multiplexed NGS libraries with unique molecular identifiers.

Science.gov (United States)

Girardot, Charles; Scholtalbers, Jelle; Sauer, Sajoscha; Su, Shu-Yi; Furlong, Eileen E M

2016-10-08

The yield obtained from next generation sequencers has increased almost exponentially in recent years, making sample multiplexing common practice. While barcodes (known sequences of fixed length) primarily encode the sample identity of sequenced DNA fragments, barcodes made of random sequences (Unique Molecular Identifier or UMIs) are often used to distinguish between PCR duplicates and transcript abundance in, for example, single-cell RNA sequencing (scRNA-seq). In paired-end sequencing, different barcodes can be inserted at each fragment end to either increase the number of multiplexed samples in the library or to use one of the barcodes as UMI. Alternatively, UMIs can be combined with the sample barcodes into composite barcodes, or with standard Illumina® indexing. Subsequent analysis must take read duplicates and sample identity into account, by identifying UMIs. Existing tools do not support these complex barcoding configurations and custom code development is frequently required. Here, we present Je, a suite of tools that accommodates complex barcoding strategies, extracts UMIs and filters read duplicates taking UMIs into account. Using Je on publicly available scRNA-seq and iCLIP data containing UMIs, the number of unique reads increased by up to 36 %, compared to when UMIs are ignored. Je is implemented in JAVA and uses the Picard API. Code, executables and documentation are freely available at http://gbcs.embl.de/Je . Je can also be easily installed in Galaxy through the Galaxy toolshed.
Designing competitions for education in robotics

DEFF Research Database (Denmark)

Andersen, Nils Axel; Ravn, Ole

2012-01-01

The paper describes design considerations for making a robot competition. Topics as level of participants, learning objective, evaluation form, task design and competition rules are treated. It is shown that careful design considering these topics are necessary for a succesful outcome of a compet......The paper describes design considerations for making a robot competition. Topics as level of participants, learning objective, evaluation form, task design and competition rules are treated. It is shown that careful design considering these topics are necessary for a succesful outcome...... of a competition. The conclusions are based on examples from more than 15 years of experience with robotic competitions....
A Human-Robot Interaction Perspective on Assistive and Rehabilitation Robotics.

Science.gov (United States)

Beckerle, Philipp; Salvietti, Gionata; Unal, Ramazan; Prattichizzo, Domenico; Rossi, Simone; Castellini, Claudio; Hirche, Sandra; Endo, Satoshi; Amor, Heni Ben; Ciocarlie, Matei; Mastrogiovanni, Fulvio; Argall, Brenna D; Bianchi, Matteo

2017-01-01

Assistive and rehabilitation devices are a promising and challenging field of recent robotics research. Motivated by societal needs such as aging populations, such devices can support motor functionality and subject training. The design, control, sensing, and assessment of the devices become more sophisticated due to a human in the loop. This paper gives a human-robot interaction perspective on current issues and opportunities in the field. On the topic of control and machine learning, approaches that support but do not distract subjects are reviewed. Options to provide sensory user feedback that are currently missing from robotic devices are outlined. Parallels between device acceptance and affective computing are made. Furthermore, requirements for functional assessment protocols that relate to real-world tasks are discussed. In all topic areas, the design of human-oriented frameworks and methods is dominated by challenges related to the close interaction between the human and robotic device. This paper discusses the aforementioned aspects in order to open up new perspectives for future robotic solutions.
Developing a successful robotics program.

Science.gov (United States)

Luthringer, Tyler; Aleksic, Ilija; Caire, Arthur; Albala, David M

2012-01-01

Advancements in the robotic surgical technology have revolutionized the standard of care for many surgical procedures. The purpose of this review is to evaluate the important considerations in developing a new robotics program at a given healthcare institution. Patients' interest in robotic-assisted surgery has and continues to grow because of improved outcomes and decreased periods of hospitalization. Resulting market forces have created a solid foundation for the implementation of robotic surgery into surgical practice. Given proper surgeon experience and an efficient system, robotic-assisted procedures have been cost comparable to open surgical alternatives. Surgeon training and experience is closely linked to the efficiency of a new robotics program. Formally trained robotic surgeons have better patient outcomes and shorter operative times. Training in robotics has shown no negative impact on patient outcomes or mentor learning curves. Individual economic factors of local healthcare settings must be evaluated when planning for a new robotics program. The high cost of the robotic surgical platform is best offset with a large surgical volume. A mature, experienced surgeon is integral to the success of a new robotics program.
Robots and Humans in Planetary Exploration: Working Together?

Science.gov (United States)

Landis, Geoffrey A.; Lyons, Valerie (Technical Monitor)

2002-01-01

Today's approach to human-robotic cooperation in planetary exploration focuses on using robotic probes as precursors to human exploration. A large portion of current NASA planetary surface exploration is focussed on Mars, and robotic probes are seen as precursors to human exploration in: Learning about operation and mobility on Mars; Learning about the environment of Mars; Mapping the planet and selecting landing sites for human mission; Demonstration of critical technology; Manufacture fuel before human presence, and emplace elements of human-support infrastructure
Sub-processes of motor learning revealed by a robotic manipulandum for rodents.

Science.gov (United States)

Lambercy, O; Schubring-Giese, M; Vigaru, B; Gassert, R; Luft, A R; Hosp, J A

2015-02-01

Rodent models are widely used to investigate neural changes in response to motor learning. Usually, the behavioral readout of motor learning tasks used for this purpose is restricted to a binary measure of performance (i.e. "successful" movement vs. "failure"). Thus, the assignability of research in rodents to concepts gained in human research - implying diverse internal models that constitute motor learning - is still limited. To solve this problem, we recently introduced a three-degree-of-freedom robotic platform designed for rats (the ETH-Pattus) that combines an accurate behavioral readout (in the form of kinematics) with the possibility to invasively assess learning related changes within the brain (e.g. by performing immunohistochemistry or electrophysiology in acute slice preparations). Here, we validate this platform as a tool to study motor learning by establishing two forelimb-reaching paradigms that differ in degree of skill. Both conditions can be precisely differentiated in terms of their temporal pattern and performance levels. Based on behavioral data, we hypothesize the presence of several sub-processes contributing to motor learning. These share close similarities with concepts gained in humans or primates. Copyright © 2014 Elsevier B.V. All rights reserved.
Discovering Binomial Identities with PascGaloisJE

Science.gov (United States)

Evans, Tyler J.

2008-01-01

We describe exercises in which students use PascGaloisJE to formulate conjectures about certain binomial identities which hold when the binomial coefficients are interpreted as elements in the cyclic group Z[subscript p] of integers modulo a prime integer "p". In addition to having an appealing visual component, these exercises are open-ended and…
Omslaget - hvid okseøje (Leucanthemum vulgare)

DEFF Research Database (Denmark)

Friis, Ib

2010-01-01

Artiklen redergør for den historiske baggrund for den illustration (tavle 994 fra Flora Danica, udgivet i 1790) af hvid okseøje (Leucanthemum vulgare), der er anvendt som illustration på festskriftets omslag. De illustratorer, der blev anvendt af Martin Vahl, udgiver af Flora Danica da tavle 994...
Robotic education, a tool for the theaching-learning of the science and technology

Directory of Open Access Journals (Sweden)

Kathia Pittí Patiño

2012-07-01

Full Text Available Normal.dotm 0 0 1 113 649 Universidad de Salamanca 5 1 797 12.0 0 false 18 pt 18 pt 0 0 false false false /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} This paper presents and analyzes the educational robotics as a tool to support teaching and learning process at the level of pre-media, primarily engaged in complex subjects such as mathematics, physics and computer science, among others. The study was limited to high schools in the province of Chiriqui, Panama, took a sample of six schools in the province and for each school involved both students and teachers. The main objective of the project was to demonstrate how robotics education, facilitates and encourages teaching and learning of science and technology. The results showed that robotics could become an excellent tool to understand abstract concepts and complex subjects in the area of science and technology, as well as allowing developing basic skills such as teamwork.
A study on an autonomous pipeline maintenance robot, 8

International Nuclear Information System (INIS)

Fukuda, Toshio; Hosokai, Hidemi; Niitsu, Shunichi; Kaneshige, Masanori; Iwasaki, Shinnosuke.

1990-01-01

This paper deals with the path planning and sensing planning expert system with learning functions for the pipeline inspection and maintenance robot, Mark IV. The robot can carry out inspection tasks to autonomously detect malfunctions in a plant pipeline system. Furthermore, the robot becomes more intelligent by adding the following functions: (1) the robot, Mark IV, is capable of inspecting surfaces of storage tanks as well as pipeline outer surfaces; (2) in path planning, the robot has a learning function using information generated in the past such as a moving path, task level and control commands of the robot; (3) in inspecting a pipeline system with plant equipment such as valves, franges, T- and L-joints, the robot is capable of inspecting continuous surfaces in pipeline. Thus, together with the improved path planning expert system (PPES) and the sensing planning expert system (SPES), the Mark IV robot becomes intelligent enough to automatically carry out given inspection tasks. (author)
A Multi-Agent Control Architecture for a Robotic Wheelchair

Directory of Open Access Journals (Sweden)

C. Galindo

2006-01-01

Full Text Available Assistant robots like robotic wheelchairs can perform an effective and valuable work in our daily lives. However, they eventually may need external help from humans in the robot environment (particularly, the driver in the case of a wheelchair to accomplish safely and efficiently some tricky tasks for the current technology, i.e. opening a locked door, traversing a crowded area, etc. This article proposes a control architecture for assistant robots designed under a multi-agent perspective that facilitates the participation of humans into the robotic system and improves the overall performance of the robot as well as its dependability. Within our design, agents have their own intentions and beliefs, have different abilities (that include algorithmic behaviours and human skills and also learn autonomously the most convenient method to carry out their actions through reinforcement learning. The proposed architecture is illustrated with a real assistant robot: a robotic wheelchair that provides mobility to impaired or elderly people.
Creating and maintaining chemical artificial life by robotic symbiosis

DEFF Research Database (Denmark)

Hanczyc, Martin M.; Parrilla, Juan M.; Nicholson, Arwen

2015-01-01

We present a robotic platform based on the open source RepRap 3D printer that can print and maintain chemical artificial life in the form of a dynamic, chemical droplet. The robot uses computer vision, a self-organizing map, and a learning program to automatically categorize the behavior of the d......We present a robotic platform based on the open source RepRap 3D printer that can print and maintain chemical artificial life in the form of a dynamic, chemical droplet. The robot uses computer vision, a self-organizing map, and a learning program to automatically categorize the behavior...... confluence of chemical, artificial intelligence, and robotic approaches to artificial life....
Creating and Maintaining Chemical Artificial Life by Robotic Symbiosis

DEFF Research Database (Denmark)

Hanczyc, Martin; Parrilla, Juan M.; Nicholson, Arwen

2015-01-01

We present a robotic platform based on the open source RepRap 3D printer that can print and maintain chemical artificial life in the form of a dynamic, chemical droplet. The robot uses computer vision, a self-organizing map, and a learning program to automatically categorize the behavior of the d......We present a robotic platform based on the open source RepRap 3D printer that can print and maintain chemical artificial life in the form of a dynamic, chemical droplet. The robot uses computer vision, a self-organizing map, and a learning program to automatically categorize the behavior...... confluence of chemical, artificial intelligence, and robotic approaches to artificial life....

Smart learning objects for smart education in computer science theory, methodology and robot-based implementation

CERN Document Server

Stuikys, Vytautas

2015-01-01

This monograph presents the challenges, vision and context to design smart learning objects (SLOs) through Computer Science (CS) education modelling and feature model transformations. It presents the latest research on the meta-programming-based generative learning objects (the latter with advanced features are treated as SLOs) and the use of educational robots in teaching CS topics. The introduced methodology includes the overall processes to develop SLO and smart educational environment (SEE) and integrates both into the real education setting to provide teaching in CS using constructivist a
Spacio-temporal situation assessment for mobile robots

DEFF Research Database (Denmark)

Beck, Anders Billesø; Risager, Claus; Andersen, Nils Axel

2011-01-01

chains are used to model the situation states and sequence, where stream clustering is used for state matching and dealing with noise. In experiments using simulated and real data, we show that we are able to learn a situation sequence for a mobile robot passing through a narrow passage. After learning......In this paper, we present a framework for situation modeling and assessment for mobile robot applications. We consider situations as data patterns that characterize unique circumstances for the robot, and represented not only by the data but also its temporal and spacial sequence. Dynamic Markov...
Outcomes from the Delphi process of the Thoracic Robotic Curriculum Development Committee.

Science.gov (United States)

Veronesi, Giulia; Dorn, Patrick; Dunning, Joel; Cardillo, Giuseppe; Schmid, Ralph A; Collins, Justin; Baste, Jean-Marc; Limmer, Stefan; Shahin, Ghada M M; Egberts, Jan-Hendrik; Pardolesi, Alessandro; Meacci, Elisa; Stamenkovic, Sasha; Casali, Gianluca; Rueckert, Jens C; Taurchini, Mauro; Santelmo, Nicola; Melfi, Franca; Toker, Alper

2018-06-01

As the adoption of robotic procedures becomes more widespread, additional risk related to the learning curve can be expected. This article reports the results of a Delphi process to define procedures to optimize robotic training of thoracic surgeons and to promote safe performance of established robotic interventions as, for example, lung cancer and thymoma surgery. In June 2016, a working panel was spontaneously created by members of the European Society of Thoracic Surgeons (ESTS) and European Association for Cardio-Thoracic Surgery (EACTS) with a specialist interest in robotic thoracic surgery and/or surgical training. An e-consensus-finding exercise using the Delphi methodology was applied requiring 80% agreement to reach consensus on each question. Repeated iterations of anonymous voting continued over 3 rounds. Agreement was reached on many points: a standardized robotic training curriculum for robotic thoracic surgery should be divided into clearly defined sections as a staged learning pathway; the basic robotic curriculum should include a baseline evaluation, an e-learning module, a simulation-based training (including virtual reality simulation, Dry lab and Wet lab) and a robotic theatre (bedside) observation. Advanced robotic training should include e-learning on index procedures (right upper lobe) with video demonstration, access to video library of robotic procedures, simulation training, modular console training to index procedure, transition to full-procedure training with a proctor and final evaluation of the submitted video to certified independent examiners. Agreement was reached on a large number of questions to optimize and standardize training and education of thoracic surgeons in robotic activity. The production of the content of the learning material is ongoing.
Architecture for robot intelligence

Science.gov (United States)

Peters, II, Richard Alan (Inventor)

2004-01-01

An architecture for robot intelligence enables a robot to learn new behaviors and create new behavior sequences autonomously and interact with a dynamically changing environment. Sensory information is mapped onto a Sensory Ego-Sphere (SES) that rapidly identifies important changes in the environment and functions much like short term memory. Behaviors are stored in a DBAM that creates an active map from the robot's current state to a goal state and functions much like long term memory. A dream state converts recent activities stored in the SES and creates or modifies behaviors in the DBAM.
Lessons of nuclear robot history

International Nuclear Information System (INIS)

Oomichi, Takeo

2014-01-01

Severe accidents occurred at Fukushima Daiichi Nuclear Power Station stirred up people's great expectation of nuclear robot's deployment. However unexpected nuclear disaster, especially rupture of reactor building caused by core meltdown and hydrogen explosion, made it quite difficult to introduce nuclear robot under high radiation environment to cease accidents and dispose damaged reactor. Robotics Society of Japan (RSJ) set up committee to look back upon lessons learned from 50 year's past experience of nuclear robot development and summarized 'Lessons of nuclear robot history', which was shown on the home page website of RSJ. This article outlined it with personal comment. History of nuclear robot developed for inspection and maintenance at normal operation and for specific required response at nuclear accidents was reviewed with many examples at home and abroad for TMI, Chernobyl and JCO accidents. Present state of Fukushima accident response robot's introduction and development was also described with some comments on nuclear robot development from academia based on lessons. (T. Tanaka)
Robots that can adapt like animals.

Science.gov (United States)

Cully, Antoine; Clune, Jeff; Tarapore, Danesh; Mouret, Jean-Baptiste

2015-05-28

Robots have transformed many industries, most notably manufacturing, and have the power to deliver tremendous benefits to society, such as in search and rescue, disaster response, health care and transportation. They are also invaluable tools for scientific exploration in environments inaccessible to humans, from distant planets to deep oceans. A major obstacle to their widespread adoption in more complex environments outside factories is their fragility. Whereas animals can quickly adapt to injuries, current robots cannot 'think outside the box' to find a compensatory behaviour when they are damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. A promising approach to reducing robot fragility involves having robots learn appropriate behaviours in response to damage, but current techniques are slow even with small, constrained search spaces. Here we introduce an intelligent trial-and-error algorithm that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans. Before the robot is deployed, it uses a novel technique to create a detailed map of the space of high-performing behaviours. This map represents the robot's prior knowledge about what behaviours it can perform and their value. When the robot is damaged, it uses this prior knowledge to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a behaviour that compensates for the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new algorithm will enable more robust, effective, autonomous robots, and may shed light on the principles
Proposed Methodology for Application of Human-like gradual Multi-Agent Q-Learning (HuMAQ) for Multi-robot Exploration

International Nuclear Information System (INIS)

Ray, Dip Narayan; Majumder, Somajyoti

2014-01-01

Several attempts have been made by the researchers around the world to develop a number of autonomous exploration techniques for robots. But it has been always an important issue for developing the algorithm for unstructured and unknown environments. Human-like gradual Multi-agent Q-leaming (HuMAQ) is a technique developed for autonomous robotic exploration in unknown (and even unimaginable) environments. It has been successfully implemented in multi-agent single robotic system. HuMAQ uses the concept of Subsumption architecture, a well-known Behaviour-based architecture for prioritizing the agents of the multi-agent system and executes only the most common action out of all the different actions recommended by different agents. Instead of using new state-action table (Q-table) each time, HuMAQ uses the immediate past table for efficient and faster exploration. The proof of learning has also been established both theoretically and practically. HuMAQ has the potential to be used in different and difficult situations as well as applications. The same architecture has been modified to use for multi-robot exploration in an environment. Apart from all other existing agents used in the single robotic system, agents for inter-robot communication and coordination/ co-operation with the other similar robots have been introduced in the present research. Current work uses a series of indigenously developed identical autonomous robotic systems, communicating with each other through ZigBee protocol
Young Children Treat Robots as Informants.

Science.gov (United States)

Breazeal, Cynthia; Harris, Paul L; DeSteno, David; Kory Westlund, Jacqueline M; Dickens, Leah; Jeong, Sooyeon

2016-04-01

Children ranging from 3 to 5 years were introduced to two anthropomorphic robots that provided them with information about unfamiliar animals. Children treated the robots as interlocutors. They supplied information to the robots and retained what the robots told them. Children also treated the robots as informants from whom they could seek information. Consistent with studies of children's early sensitivity to an interlocutor's non-verbal signals, children were especially attentive and receptive to whichever robot displayed the greater non-verbal contingency. Such selective information seeking is consistent with recent findings showing that although young children learn from others, they are selective with respect to the informants that they question or endorse. Copyright © 2016 Cognitive Science Society, Inc.
Biologically-Inspired Adaptive Obstacle Negotiation Behavior of Hexapod Robots

Directory of Open Access Journals (Sweden)

Dennis eGoldschmidt

2014-01-01

Full Text Available Neurobiological studies have shown that insects are able to adapt leg movements and posture for obstacle negotiation in changing environments. Moreover, the distance to an obstacle where an insect begins to climb is found to be a major parameter for successful obstacle negotiation. Inspired by these findings, we present an adaptive neural control mechanism for obstacle negotiation behavior in hexapod robots. It combines locomotion control, backbone joint control, local leg reflexes, and neural learning. While the first three components generate locomotion including walking and climbing, the neural learning mechanism allows the robot to adapt its behavior for obstacle negotiation with respect to changing conditions, e.g., variable obstacle heights and different walking gaits. By successfully learning the association of an early, predictive signal (conditioned stimulus, CS and a late, reflex signal (unconditioned stimulus, UCS, both provided by ultrasonic sensors at the front of the robot, the robot can autonomously find an appropriate distance from an obstacle to initiate climbing. The adaptive neural control was developed and tested first on a physical robot simulation, and was then successfully transferred to a real hexapod robot, called AMOS II. The results show that the robot can efficiently negotiate obstacles with a height up to 85% of the robot's leg length in simulation and 75% in a real environment.
Hoe maak je gebruik van tablets voor leren?

NARCIS (Netherlands)

Kalz, Marco; Kluijfhout, Eric; Börner, Dirk; Tabuenca, Bernardo; Kester, Liesbeth; Wolff, Charlotte; Bahreini, Kiavash; Gijselaers, Jérôme; Andeweg, Cisca; De Vries, Fred; Berkhout, Jeroen; Storm, Jeroen

2012-01-01

Kalz, M., Kluijfhout, E., Börner, D., Tabuenca, B., Kester, L., Wolff, C., Bahreini, K., Gijselaers. J., De Vries, F., Andeweg, C., Berkhout, J., & Storm, J. (2012, 28 September). Hoe maak je gebruik van tablets voor leren? Masterclass in de OpenU community. Open Universiteit, Heerlen, Nederland.
The robotic appendicovesicostomy and bladder augmentation: the next frontier in robotics, are we there?

Science.gov (United States)

Cohen, Andrew J; Pariser, Joseph J; Anderson, Blake B; Pearce, Shane M; Gundeti, Mohan S

2015-02-01

There is growing interest in applying robotic-assisted laparoscopic techniques to complex reconstructive pelvic surgery owing to inherent benefits of precision, tissue handling, and articulating instruments for suturing. This review examines preliminary experiences with robotic-assisted laparoscopic augmentation ileocystoplasty and Mitrofanoff appendicovesicostomy (RALIMA) as either an isolated or combined procedure. These series suggest RALIMA is feasible, with the benefit of early recovery and improved cosmetic results in selected patients. The robotic approach incurs functional outcomes and complication rates similar to those of open techniques. Given the steep learning curve, only surgeons with extensive robotic experience are currently adopting this technique. Copyright © 2015 Elsevier Inc. All rights reserved.
Show de fysica 2 natuurkunde laat je zien

CERN Document Server

2017-01-01

Alweer een Showdefysica? Zo snel na het vorige boek? Het eerste boek is goed ontvangen, het auteursteam is nog steeds enthousiast en hun motivatie om wederom een boek te maken met vakdidactische accenten is onverminderd groot! Ideeën genoeg om ons prachtige vak extra glans te geven. Aan de slag dus met een vervolg op het in 2015 verschenen boek ShowdeFysica. Dit boek legt net als het vorige het accent op het gebruik van demonstaties in het natuurkunde-onderwijs. Telkens is er aandacht voor wat je als demonstrateur moet doen om te zorgen dat je leerlingen zoveel mogelijk leren. Natuurwetenschappelijke vaardigheden leren, natuurkundige begrippen leren en plezier beleven aan de puzzels die het vak ons biedt. Daarin is dit boek uniek. Het legt accent op vakdidactiek. De beschrijvingen steunen de docent bij het krijgen van een optimaal leereffect in de praktijk van zijn onderwijs. Veel dingen zijn in dit tweede deel van Showdefysica hetzelfde gebleven. De indeling in natuurwetenschappelijke vaardigheden (A), begr...
Evaluation of linearly solvable Markov decision process with dynamic model learning in a mobile robot navigation task

Directory of Open Access Journals (Sweden)

Ken eKinjo

2013-04-01

Full Text Available Linearly solvable Markov Decision Process (LMDP is a class of optimal control problem in whichthe Bellman’s equation can be converted into a linear equation by an exponential transformation ofthe state value function (Todorov, 2009. In an LMDP, the optimal value function and the correspondingcontrol policy are obtained by solving an eigenvalue problem in a discrete state space or an eigenfunctionproblem in a continuous state using the knowledge of the system dynamics and the action, state, andterminal cost functions.In this study, we evaluate the effectiveness of the LMDP framework in real robot control, in whichthe dynamics of the body and the environment have to be learned from experience. We first perform asimulation study of a pole swing-up task to evaluate the effect of the accuracy of the learned dynam-ics model on the derived the action policy. The result shows that a crude linear approximation of thenonlinear dynamics can still allow solution of the task, despite with a higher total cost.We then perform real robot experiments of a battery-catching task using our Spring Dog mobile robotplatform. The state is given by the position and the size of a battery in its camera view and two neck jointangles. The action is the velocities of two wheels, while the neck joints were controlled by a visual servocontroller. We test linear and bilinear dynamic models in tasks with quadratic and Guassian state costfunctions. In the quadratic cost task, the LMDP controller derived from a learned linear dynamics modelperformed equivalently with the optimal linear quadratic controller (LQR. In the non-quadratic task, theLMDP controller with a linear dynamics model showed the best performance. The results demonstratethe usefulness of the LMDP framework in real robot control even when simple linear models are usedfor dynamics learning.
Future Challenges of Robotics and Artificial Intelligence in Nursing: What Can We Learn from Monsters in Popular Culture?

Science.gov (United States)

Erikson, Henrik; Salzmann-Erikson, Martin

It is highly likely that artificial intelligence (AI) will be implemented in nursing robotics in various forms, both in medical and surgical robotic instruments, but also as different types of droids and humanoids, physical reinforcements, and also animal/pet robots. Exploring and discussing AI and robotics in nursing and health care before these tools become commonplace is of great importance. We propose that monsters in popular culture might be studied with the hope of learning about situations and relationships that generate empathic capacities in their monstrous existences. The aim of the article is to introduce the theoretical framework and assumptions behind this idea. Both robots and monsters are posthuman creations. The knowledge we present here gives ideas about how nursing science can address the postmodern, technologic, and global world to come. Monsters therefore serve as an entrance to explore technologic innovations such as AI. Analyzing when and why monsters step out of character can provide important insights into the conceptualization of caring and nursing as a science, which is important for discussing these empathic protocols, as well as more general insight into human knowledge. The relationship between caring, monsters, robotics, and AI is not as farfetched as it might seem at first glance.
A Human–Robot Interaction Perspective on Assistive and Rehabilitation Robotics

Directory of Open Access Journals (Sweden)

Philipp Beckerle

2017-05-01

Full Text Available Assistive and rehabilitation devices are a promising and challenging field of recent robotics research. Motivated by societal needs such as aging populations, such devices can support motor functionality and subject training. The design, control, sensing, and assessment of the devices become more sophisticated due to a human in the loop. This paper gives a human–robot interaction perspective on current issues and opportunities in the field. On the topic of control and machine learning, approaches that support but do not distract subjects are reviewed. Options to provide sensory user feedback that are currently missing from robotic devices are outlined. Parallels between device acceptance and affective computing are made. Furthermore, requirements for functional assessment protocols that relate to real-world tasks are discussed. In all topic areas, the design of human-oriented frameworks and methods is dominated by challenges related to the close interaction between the human and robotic device. This paper discusses the aforementioned aspects in order to open up new perspectives for future robotic solutions.
A Human–Robot Interaction Perspective on Assistive and Rehabilitation Robotics

Science.gov (United States)

Beckerle, Philipp; Salvietti, Gionata; Unal, Ramazan; Prattichizzo, Domenico; Rossi, Simone; Castellini, Claudio; Hirche, Sandra; Endo, Satoshi; Amor, Heni Ben; Ciocarlie, Matei; Mastrogiovanni, Fulvio; Argall, Brenna D.; Bianchi, Matteo

2017-01-01

Assistive and rehabilitation devices are a promising and challenging field of recent robotics research. Motivated by societal needs such as aging populations, such devices can support motor functionality and subject training. The design, control, sensing, and assessment of the devices become more sophisticated due to a human in the loop. This paper gives a human–robot interaction perspective on current issues and opportunities in the field. On the topic of control and machine learning, approaches that support but do not distract subjects are reviewed. Options to provide sensory user feedback that are currently missing from robotic devices are outlined. Parallels between device acceptance and affective computing are made. Furthermore, requirements for functional assessment protocols that relate to real-world tasks are discussed. In all topic areas, the design of human-oriented frameworks and methods is dominated by challenges related to the close interaction between the human and robotic device. This paper discusses the aforementioned aspects in order to open up new perspectives for future robotic solutions. PMID:28588473
Modeling and Simulation of Elementary Robot Behaviors using Associative Memories

Directory of Open Access Journals (Sweden)

Claude F. Touzet

2006-06-01

Full Text Available Today, there are several drawbacks that impede the necessary and much needed use of robot learning techniques in real applications. First, the time needed to achieve the synthesis of any behavior is prohibitive. Second, the robot behavior during the learning phase is – by definition – bad, it may even be dangerous. Third, except within the lazy learning approach, a new behavior implies a new learning phase. We propose in this paper to use associative memories (self-organizing maps to encode the non explicit model of the robot-world interaction sampled by the lazy memory, and then generate a robot behavior by means of situations to be achieved, i.e., points on the self-organizing maps. Any behavior can instantaneously be synthesized by the definition of a goal situation. Its performance will be minimal (not necessarily bad and will improve by the mere repetition of the behavior.
Software for Project-Based Learning of Robot Motion Planning

Science.gov (United States)

Moll, Mark; Bordeaux, Janice; Kavraki, Lydia E.

2013-01-01

Motion planning is a core problem in robotics concerned with finding feasible paths for a given robot. Motion planning algorithms perform a search in the high-dimensional continuous space of robot configurations and exemplify many of the core algorithmic concepts of search algorithms and associated data structures. Motion planning algorithms can…
Little AI: Playing a constructivist robot

Science.gov (United States)

Georgeon, Olivier L.

Little AI is a pedagogical game aimed at presenting the founding concepts of constructivist learning and developmental Artificial Intelligence. It primarily targets students in computer science and cognitive science but it can also interest the general public curious about these topics. It requires no particular scientific background; even children can find it entertaining. Professors can use it as a pedagogical resource in class or in online courses. The player presses buttons to control a simulated "baby robot". The player cannot see the robot and its environment, and initially ignores the effects of the commands. The only information received by the player is feedback from the player's commands. The player must learn, at the same time, the functioning of the robot's body and the structure of the environment from patterns in the stream of commands and feedback. We argue that this situation is analogous to how infants engage in early-stage developmental learning (e.g., Piaget (1937), [1]).
The Relative Merits of Transparency: Investigating Situations that Support the Use of Robotics in Developing Student Learning Adaptability across Virtual and Physical Computing Platforms

Science.gov (United States)

Okita, Sandra Y.

2014-01-01

This study examined whether developing earlier forms of knowledge in specific learning environments prepares students better for future learning when they are placed in an unfamiliar learning environment. Forty-one students in the fifth and sixth grades learned to program robot movements using abstract concepts of speed, distance and direction.…

Training in urological robotic surgery. Future perspectives.

Science.gov (United States)

El Sherbiny, Ahmed; Eissa, Ahmed; Ghaith, Ahmed; Morini, Elena; Marzotta, Lucilla; Sighinolfi, Maria Chiara; Micali, Salvatore; Bianchi, Giampaolo; Rocco, Bernardo

2018-01-01

As robotics are becoming more integrated into the medical field, robotic training is becoming more crucial in order to overcome the lack of experienced robotic surgeons. However, there are several obstacles facing the development of robotic training programs like the high cost of training and the increased operative time during the initial period of the learning curve, which, in turn increase the operative cost. Robotic-assisted laparoscopic prostatectomy is the most commonly performed robotic surgery. Moreover, robotic surgery is becoming more popular among urologic oncologists and pediatric urologists. The need for a standardized and validated robotic training curriculum was growing along with the increased number of urologic centers and institutes adopting the robotic technology. Robotic training includes proctorship, mentorship or fellowship, telementoring, simulators and video training. In this chapter, we are going to discuss the different training methods, how to evaluate robotic skills, the available robotic training curriculum, and the future perspectives.
The impact of robotic surgery in urology.

Science.gov (United States)

Giedelman, C A; Abdul-Muhsin, H; Schatloff, O; Palmer, K; Lee, L; Sanchez-Salas, R; Cathelineau, X; Dávila, H; Cavelier, L; Rueda, M; Patel, V

2013-01-01

More than a decade ago, robotic surgery was introduced into urology. Since then, the urological community started to look at surgery from a different angle. The present, the future hopes, and the way we looked at our past experience have all changed. Between 2000 and 2011, the published literature was reviewed using the National Library of Medicine database and the following key words: robotic surgery, robot-assisted, and radical prostatectomy. Special emphasis was given to the impact of the robotic surgery in urology. We analyzed the most representative series (finished learning curve) in each one of the robotic approaches regarding perioperative morbidity and oncological outcomes. This article looks into the impact of robotics in urology, starting from its background applications before urology, the way it was introduced into urology, its first steps, current status, and future expectations. By narrating this journey, we tried to highlight important modifications that helped robotic surgery make its way to its position today. We looked as well into the dramatic changes that robotic surgery introduced to the field of surgical training and its consequence on its learning curve. Basic surgical principles still apply in Robotics: experience counts, and prolonged practice provides knowledge and skills. In this way, the potential advantages delivered by technology will be better exploited, and this will be reflected in better outcomes for patients. Copyright © 2012 AEU. Published by Elsevier Espana. All rights reserved.
Automatic planning for robots: review of methods and some ideas about structure and learning

Energy Technology Data Exchange (ETDEWEB)

Cuena, J.; Salmeron, C.

1983-01-01

After a brief review of the problems involved in the design of an automatic planner system, the attention is focused in the particular problems that appear when the planner is used to control the actions of a robot. As conclusion, the introduction of techniques for learning in order to improve the efficiency of a planner are suggested, and a method for it, at present in development, is presented. 14 references.
Effects of different kinds of robot feedback

DEFF Research Database (Denmark)

Fischer, Kerstin; Lohan, K. S.; Nehaniv, C.

2013-01-01

, we investigate the impact of the robot's learning success on tutors' tutoring strategies. Our results show that only in the condition in which the robot's behavior is socially contingent, the human tutors adjust their behavior to the robot. In the developmentally equally plausible object......In this paper, we investigate to what extent tutors' behavior is influenced by different kinds of robot feedback. In particular, we study the effects of online robot feedback in which the robot responds either contingently to the tutor's social behavior or by tracking the objects presented. Also......-driven condition, in which the robot tracked the objects presented, tutors do not change their behavior significantly, even though in both conditions the robot develops from a prelinguistic stage to producing keywords. Socially contingent robot feedback has thus the potential to influence tutors' behavior over...
Application of the statistical process control method for prospective patient safety monitoring during the learning phase: robotic kidney transplantation with regional hypothermia (IDEAL phase 2a-b).

Science.gov (United States)

Sood, Akshay; Ghani, Khurshid R; Ahlawat, Rajesh; Modi, Pranjal; Abaza, Ronney; Jeong, Wooju; Sammon, Jesse D; Diaz, Mireya; Kher, Vijay; Menon, Mani; Bhandari, Mahendra

2014-08-01

Traditional evaluation of the learning curve (LC) of an operation has been retrospective. Furthermore, LC analysis does not permit patient safety monitoring. To prospectively monitor patient safety during the learning phase of robotic kidney transplantation (RKT) and determine when it could be considered learned using the techniques of statistical process control (SPC). From January through May 2013, 41 patients with end-stage renal disease underwent RKT with regional hypothermia at one of two tertiary referral centers adopting RKT. Transplant recipients were classified into three groups based on the robotic training and kidney transplant experience of the surgeons: group 1, robot trained with limited kidney transplant experience (n=7); group 2, robot trained and kidney transplant experienced (n=20); and group 3, kidney transplant experienced with limited robot training (n=14). We employed prospective monitoring using SPC techniques, including cumulative summation (CUSUM) and Shewhart control charts, to perform LC analysis and patient safety monitoring, respectively. Outcomes assessed included post-transplant graft function and measures of surgical process (anastomotic and ischemic times). CUSUM and Shewhart control charts are time trend analytic techniques that allow comparative assessment of outcomes following a new intervention (RKT) relative to those achieved with established techniques (open kidney transplant; target value) in a prospective fashion. CUSUM analysis revealed an initial learning phase for group 3, whereas groups 1 and 2 had no to minimal learning time. The learning phase for group 3 varied depending on the parameter assessed. Shewhart control charts demonstrated no compromise in functional outcomes for groups 1 and 2. Graft function was compromised in one patient in group 3 (pcontrol chart analytic techniques. These methods allow determination of the duration of mentorship and identification of adverse events in a timely manner. A new operation
Reward-Modulated Hebbian Plasticity as Leverage for Partially Embodied Control in Compliant Robotics

Science.gov (United States)

Burms, Jeroen; Caluwaerts, Ken; Dambre, Joni

2015-01-01

In embodied computation (or morphological computation), part of the complexity of motor control is offloaded to the body dynamics. We demonstrate that a simple Hebbian-like learning rule can be used to train systems with (partial) embodiment, and can be extended outside of the scope of traditional neural networks. To this end, we apply the learning rule to optimize the connection weights of recurrent neural networks with different topologies and for various tasks. We then apply this learning rule to a simulated compliant tensegrity robot by optimizing static feedback controllers that directly exploit the dynamics of the robot body. This leads to partially embodied controllers, i.e., hybrid controllers that naturally integrate the computations that are performed by the robot body into a neural network architecture. Our results demonstrate the universal applicability of reward-modulated Hebbian learning. Furthermore, they demonstrate the robustness of systems trained with the learning rule. This study strengthens our belief that compliant robots should or can be seen as computational units, instead of dumb hardware that needs a complex controller. This link between compliant robotics and neural networks is also the main reason for our search for simple universal learning rules for both neural networks and robotics. PMID:26347645
Acquisition of earthworm-like movement patterns of many-segmented peristaltic crawling robots

Directory of Open Access Journals (Sweden)

Norihiko Saga

2016-09-01

Full Text Available In recent years, attention has been increasingly devoted to the development of rescue robots that can protect humans from the inherent risks of rescue work. Particularly, anticipated is the development of a robot that can move deeply through small spaces. We have devoted our attention to peristalsis, the movement mechanism used by earthworms. A reinforcement learning technique used for the derivation of the robot movement pattern, Q-learning, was used to develop a three-segmented peristaltic crawling robot with a motor drive. Characteristically, peristalsis can provide movement capability if at least three segments work, even if a segmented part does not function. Therefore, we had intended to derive the movement pattern of many-segmented peristaltic crawling robots using Q-learning. However, because of the necessary increase in calculations, in the case of many segments, Q-learning cannot be used because of insufficient memory. Therefore, we devoted our attention to a learning method called Actor–Critic, which can be implemented with low memory. Because Actor-Critic methods are TD methods that have a separate memory structure to explicitly represent the policy independent of the value function. Using it, we examined the movement patterns of six-segmented peristaltic crawling robots.
Human-Robot Interaction: Does Robotic Guidance Force Affect Gait-Related Brain Dynamics during Robot-Assisted Treadmill Walking?

Directory of Open Access Journals (Sweden)

Kristel Knaepen

Full Text Available In order to determine optimal training parameters for robot-assisted treadmill walking, it is essential to understand how a robotic device interacts with its wearer, and thus, how parameter settings of the device affect locomotor control. The aim of this study was to assess the effect of different levels of guidance force during robot-assisted treadmill walking on cortical activity. Eighteen healthy subjects walked at 2 km.h-1 on a treadmill with and without assistance of the Lokomat robotic gait orthosis. Event-related spectral perturbations and changes in power spectral density were investigated during unassisted treadmill walking as well as during robot-assisted treadmill walking at 30%, 60% and 100% guidance force (with 0% body weight support. Clustering of independent components revealed three clusters of activity in the sensorimotor cortex during treadmill walking and robot-assisted treadmill walking in healthy subjects. These clusters demonstrated gait-related spectral modulations in the mu, beta and low gamma bands over the sensorimotor cortex related to specific phases of the gait cycle. Moreover, mu and beta rhythms were suppressed in the right primary sensory cortex during treadmill walking compared to robot-assisted treadmill walking with 100% guidance force, indicating significantly larger involvement of the sensorimotor area during treadmill walking compared to robot-assisted treadmill walking. Only marginal differences in the spectral power of the mu, beta and low gamma bands could be identified between robot-assisted treadmill walking with different levels of guidance force. From these results it can be concluded that a high level of guidance force (i.e., 100% guidance force and thus a less active participation during locomotion should be avoided during robot-assisted treadmill walking. This will optimize the involvement of the sensorimotor cortex which is known to be crucial for motor learning.
Human-Robot Interaction: Does Robotic Guidance Force Affect Gait-Related Brain Dynamics during Robot-Assisted Treadmill Walking?

Science.gov (United States)

Knaepen, Kristel; Mierau, Andreas; Swinnen, Eva; Fernandez Tellez, Helio; Michielsen, Marc; Kerckhofs, Eric; Lefeber, Dirk; Meeusen, Romain

2015-01-01

In order to determine optimal training parameters for robot-assisted treadmill walking, it is essential to understand how a robotic device interacts with its wearer, and thus, how parameter settings of the device affect locomotor control. The aim of this study was to assess the effect of different levels of guidance force during robot-assisted treadmill walking on cortical activity. Eighteen healthy subjects walked at 2 km.h-1 on a treadmill with and without assistance of the Lokomat robotic gait orthosis. Event-related spectral perturbations and changes in power spectral density were investigated during unassisted treadmill walking as well as during robot-assisted treadmill walking at 30%, 60% and 100% guidance force (with 0% body weight support). Clustering of independent components revealed three clusters of activity in the sensorimotor cortex during treadmill walking and robot-assisted treadmill walking in healthy subjects. These clusters demonstrated gait-related spectral modulations in the mu, beta and low gamma bands over the sensorimotor cortex related to specific phases of the gait cycle. Moreover, mu and beta rhythms were suppressed in the right primary sensory cortex during treadmill walking compared to robot-assisted treadmill walking with 100% guidance force, indicating significantly larger involvement of the sensorimotor area during treadmill walking compared to robot-assisted treadmill walking. Only marginal differences in the spectral power of the mu, beta and low gamma bands could be identified between robot-assisted treadmill walking with different levels of guidance force. From these results it can be concluded that a high level of guidance force (i.e., 100% guidance force) and thus a less active participation during locomotion should be avoided during robot-assisted treadmill walking. This will optimize the involvement of the sensorimotor cortex which is known to be crucial for motor learning.
Future of robotic surgery.

Science.gov (United States)

Lendvay, Thomas Sean; Hannaford, Blake; Satava, Richard M

2013-01-01

In just over a decade, robotic surgery has penetrated almost every surgical subspecialty and has even replaced some of the most commonly performed open oncologic procedures. The initial reports on patient outcomes yielded mixed results, but as more medical centers develop high-volume robotics programs, outcomes appear comparable if not improved for some applications. There are limitations to the current commercially available system, and new robotic platforms, some designed to compete in the current market and some to address niche surgical considerations, are being developed that will change the robotic landscape in the next decade. Adoption of these new systems will be dependent on overcoming barriers to true telesurgery that range from legal to logistical. As additional surgical disciplines embrace robotics and open surgery continues to be replaced by robotic approaches, it will be imperative that adequate education and training keep pace with technology. Methods to enhance surgical performance in robotics through the use of simulation and telementoring promise to accelerate learning curves and perhaps even improve surgical readiness through brief virtual-reality warm-ups and presurgical rehearsal. All these advances will need to be carefully and rigorously validated through not only patient outcomes, but also cost efficiency.
The evolution of robotic general surgery.

Science.gov (United States)

Wilson, E B

2009-01-01

Surgical robotics in general surgery has a relatively short but very interesting evolution. Just as minimally invasive and laparoscopic techniques have radically changed general surgery and fractionated it into subspecialization, robotic technology is likely to repeat the process of fractionation even further. Though it appears that robotics is growing more quickly in other specialties, the changes digital platforms are causing in the general surgical arena are likely to permanently alter general surgery. This review examines the evolution of robotics in minimally invasive general surgery looking forward to a time where robotics platforms will be fundamental to elective general surgery. Learning curves and adoption techniques are explored. Foregut, hepatobiliary, endocrine, colorectal, and bariatric surgery will be examined as growth areas for robotics, as well as revealing the current uses of this technology.
The Da Vinci Xi and robotic radical prostatectomy-an evolution in learning and technique.

Science.gov (United States)

Goonewardene, S S; Cahill, D

2017-06-01

The da Vinci Xi robot has been introduced as the successor to the Si platform. The promise of the Xi is to open the door to new surgical procedures. For robotic-assisted radical prostatectomy (RARP)/pelvic surgery, the potential is better vision and longer instruments. How has the Xi impacted on operative and pathological parameters as indicators of surgical performance? This is a comparison of an initial series of 42 RARPs with the Xi system in 2015 with a series using the Si system immediately before Xi uptake in the same calendar year, and an Si series by the same surgeon synchronously as the Xi series using operative time, blood loss, and positive margins as surrogates of surgical performance. Subjectively and objectively, there is a learning curve to Xi uptake in longer operative times but no impact on T2 positive margins which are the most reflective single measure of RARP outcomes. Subjectively, the vision of the Xi is inferior to the Si system, and the integrated diathermy system and automated setup are quirky. All require experience to overcome. There is a learning curve to progress from the Si to Xi da Vinci surgical platforms, but this does not negatively impact the outcome.
Making fingers and words count in a cognitive robot

Directory of Open Access Journals (Sweden)

Vivian Milagros De La Cruz

2014-02-01

Full Text Available Evidence from developmental as well as neuroscientific studies suggest that finger counting activity plays an important role in the acquisition of numerical skills in children. It has been claimed that this skill helps in building motor-based representations of number that continue to influence number processing well into adulthood, facilitating the emergence of number concepts from sensorimotor experience through a bottom-up process. The act of counting also involves the acquisition and use of a verbal number system of which number words are the basic building blocks. Using a Cognitive Developmental Robotics paradigm we present results of a modeling experiment on whether finger counting and the association of number words (or tags to fingers, could serve to bootstrap the representation of number in a cognitive robot, enabling it to perform basic numerical operations such as addition. The cognitive architecture of the robot is based on artificial neural networks, which enable the robot to learn both sensorimotor skills (finger counting and linguistic skills (using number words. The results obtained in our experiments show that learning the number words in sequence along with finger configurations helps the fast building of the initial representation of number in the robot. Number knowledge, is instead, not as efficiently developed when number words are learned out of sequence without finger counting. Furthermore, the internal representations of the finger configurations themselves, developed by the robot as a result of the experiments, sustain the execution of basic arithmetic operations, something consistent with evidence coming from developmental research with children. The model and experiments demonstrate the importance of sensorimotor skill learning in robots for the acquisition of abstract knowledge such as numbers.
Experiences in Developing an Experimental Robotics Course Program for Undergraduate Education

Science.gov (United States)

Jung, Seul

2013-01-01

An interdisciplinary undergraduate-level robotics course offers students the chance to integrate their engineering knowledge learned throughout their college years by building a robotic system. Robotics is thus a core course in system and control-related engineering education. This paper summarizes the experience of developing robotics courses…
Robot Control Overview: An Industrial Perspective

Directory of Open Access Journals (Sweden)

T. Brogårdh

2009-07-01

Full Text Available One key competence for robot manufacturers is robot control, defined as all the technologies needed to control the electromechanical system of an industrial robot. By means of modeling, identification, optimization, and model-based control it is possible to reduce robot cost, increase robot performance, and solve requirements from new automation concepts and new application processes. Model-based control, including kinematics error compensation, optimal servo reference- and feed-forward generation, and servo design, tuning, and scheduling, has meant a breakthrough for the use of robots in industry. Relying on this breakthrough, new automation concepts such as high performance multi robot collaboration and human robot collaboration can be introduced. Robot manufacturers can build robots with more compliant components and mechanical structures without loosing performance and robots can be used also in applications with very high performance requirements, e.g., in assembly, machining, and laser cutting. In the future it is expected that the importance of sensor control will increase, both with respect to sensors in the robot structure to increase the control performance of the robot itself and sensors outside the robot related to the applications and the automation systems. In this connection sensor fusion and learning functionalities will be needed together with the robot control for easy and intuitive installation, programming, and maintenance of industrial robots.
Autodesk Robot Structural Analysis Professional 2016 essentials

CERN Document Server

Marsh, Ken

2016-01-01

Autodesk Robot Structural Analysis Professional 2016 - Essentials is an excellent introduction to the essential features, functions, and workflows of Autodesk Robot Structural Analysis Professional. Master the tools you will need to make Robot work for you: Go from zero to proficiency with this thorough and detailed introduction to the essential concepts and workflows of Robot Structural Analysis Professional 2016. - Demystify the interface - Manipulate and manage Robot tables like a pro - Learn how to use Robot's modeling tools - Master loading techniques - Harness Robot automated load combinations - Decipher simplified seismic loading - Discover workflows for steel and concrete design - Gain insights to help troubleshoot issues Guided exercises are provided to help cement fundamental concepts in Robot Structural Analysis and drive home key functions. Get up to speed quickly with this essential text and add Robot Structural Analysis Professional 2016 to your analysis and design toolbox. New in 2016: AWC-NDS ...
A Study on the Education Assistant System Using Smartphones and Service Robots for Children

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Gu-Min Jeong

2014-04-01

Full Text Available In this paper, we propose a new education assistant system model using both smartphones and service robots for children's learning. Through the interaction between a smartphone and a robot, various use cases can be derived. For example, we can control the movement of the robot remotely, watch the status of the children using real-time streaming, or read the answer on the smartphone while only the question is displayed on the robot. Considering these facts, we present three use cases, namely ‘remote control’, ‘streaming’ and ‘N-screen’ for robot-based learning with smartphones. The proposed learning model is implemented in Android-based smartphones and a service robot using the OPRoS platform, and we show that the proposed model works well.1
Online Gait Learning for Modular Robots with Arbitrary Shapes and Sizes

NARCIS (Netherlands)

Weel, Berend; D'Angelo, M.; Haasdijk, Evert; Eiben, A. E.

2017-01-01

Evolutionary robotics using real hardware is currently restricted to evolving robot controllers, but the technology for evolvable morphologies is advancing quickly. Rapid prototyping (3D printing) and automated assembly are the main enablers of robotic systems where robot offspring can be produced
Humanoid Robots in the Classroom

DEFF Research Database (Denmark)

Majgaard, Gunver

2015-01-01

Humanoid robots have been used as educational tools in primary and lower secondary schools. The students involved were between 11 and 16 years old. The learning goals included: programming, language learning, ethics, technology and mathematics, e.g. practised by 7th grade students who programmed...
7th International Conference on Robotics in Education (RiE)

CERN Document Server

Lepuschitz, Wilfried; Koppensteiner, Gottfried; Balogh, Richard

2017-01-01

This proceedings volume showcases the latest achievements in research and development in Educational Robotics presented at the 7th International Conference on Robotics in Education (RiE) held in Vienna, Austria, during April 14-15, 2016. The book offers a range of methodologies for teaching robotics and presents various educational robotics curricula. It includes dedicated chapters for the design and analysis of learning environments as well as evaluation means for measuring the impact of robotics on the students’ learning success. Moreover, the book presents interesting programming approaches as well as new applications, the latest tools, systems and components for using robotics. The presented applications cover the whole educative range, from elementary school to high school, college, university and beyond, for continuing education and possibly outreach and workforce development. The book provides a framework involving two complementary kinds of contributions: on the one hand on technical aspects and on ...

UWSim, an underwater robotic simulator on the cloud as educational tool

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Javier Pérez

2017-12-01

Full Text Available Due to the introduction of robotic applications in the modern society, such as service robots or self-driving cars, it is possible to use this trend as motivating factor in the learning process of robotics. Several possibilities about how to use this motivation to increase learning rate are analysed, focusing on underwater robotic simulators. Moreover, a cloud learning environment able to evaluate the students with a robotic simulator is proposed as key element of the system. These kinds of tools can be used with just an Internet-capable system through a web browser, reaching a virtually unlimited amount of resources. The implemented features are used in a underwater pipe following application, creating a comparison environment on the cloud that immerse students in a competition to reach the best possible result. Finally, a first experience in a real educational environment using the proposed tool is detailed, demonstrating the viability and suitability of the proposed tool.
Robotic Teaching Assistance for the "Tower of Hanoi" Problem

Science.gov (United States)

Thien, Nguyen Duc; Terracina, Annalisa; Iocchi, Luca; Mecella, Massimo

2016-01-01

In this work the authors investigate the effectiveness of robotics in education. Rather than creating excitement for children when playing with robots in games, they are examining the overall learning environment where a robot acts as a teaching assistant. They designed a suitable lesson plan when groups of teenagers participate in activities…
The Effectiveness of Simulated Robots for Supporting the Learning of Introductory Programming: A Multi-Case Case Study

Science.gov (United States)

Major, Louis; Kyriacou, Theocharis; Brereton, Pearl

2014-01-01

This work investigates the effectiveness of simulated robots as tools to support the learning of programming. After the completion of a systematic review and exploratory research, a multi-case case study was undertaken. A simulator, named Kebot, was developed and used to run four 10-hour programming workshops. Twenty-three student participants…
Optimalisasi Ukuran Manipulabilitas Robot Stanford Menggunakan Metode Pseudo-inverse

OpenAIRE

admin, Gina Fahrina

2013-01-01

Robot is one of the most important element in the industrial world which has been growing very rapidly. Stanford robot arm is one of robot that use in industry, it has five degrees of freedom (DOF). Movement of the robot arm in his workspace called manipulability or manipulability measure. More the optimal manipulability measure manipulator, the more movement of the robotic arm will be more flexible in his workspace. The purpose of this research are to get knowledge and learn how to solve inv...
Robots in Elderly Care

Directory of Open Access Journals (Sweden)

Alessandro Vercelli

2018-03-01

new signs and symptoms through artificial intelligence by machine learning and deep learning and about his/her habitat. On the other, this powerful instrument may represent a dramatic treat to the privacy of the subjects and their caregivers. Therefore, robotics represents an ethically sensitive field. Care robotics bear the risk of reducing human contact, of increasing the objectification and loss of control of the elderly, of losing the privacy and personal freedom of the individual (especially when robots may perform restrictive interventions. Moreover, the use of robots in elderly care may raise in the risk of confusing between reality and appearance, with a potential risk of deception and infantilization of the elder.
Robot-assisted gait training for stroke patients: current state of the art and perspectives of robotics

Directory of Open Access Journals (Sweden)

Morone G

2017-05-01

Full Text Available Giovanni Morone,1,2 Stefano Paolucci,1,2 Andrea Cherubini,3 Domenico De Angelis,1 Vincenzo Venturiero,1 Paola Coiro,1 Marco Iosa1,2 1Private Inpatient Unit, 2Clinical Laboratory of Experimental Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy; 3Department of Robotics, LIRMM UM-CNRS, Montpellier, France Abstract: In this review, we give a brief outline of robot-mediated gait training for stroke patients, as an important emerging field in rehabilitation. Technological innovations are allowing rehabilitation to move toward more integrated processes, with improved efficiency and less long-term impairments. In particular, robot-mediated neurorehabilitation is a rapidly advancing field, which uses robotic systems to define new methods for treating neurological injuries, especially stroke. The use of robots in gait training can enhance rehabilitation, but it needs to be used according to well-defined neuroscientific principles. The field of robot-mediated neurorehabilitation brings challenges to both bioengineering and clinical practice. This article reviews the state of the art (including commercially available systems and perspectives of robotics in poststroke rehabilitation for walking recovery. A critical revision, including the problems at stake regarding robotic clinical use, is also presented. Keywords: exoskeleton, neurorehabilitation, robot-assisted walking training, wearable robot, activities of daily living, motor learning, plasticity
Medical robotics.

Science.gov (United States)

Ferrigno, Giancarlo; Baroni, Guido; Casolo, Federico; De Momi, Elena; Gini, Giuseppina; Matteucci, Matteo; Pedrocchi, Alessandra

2011-01-01

Information and communication technology (ICT) and mechatronics play a basic role in medical robotics and computer-aided therapy. In the last three decades, in fact, ICT technology has strongly entered the health-care field, bringing in new techniques to support therapy and rehabilitation. In this frame, medical robotics is an expansion of the service and professional robotics as well as other technologies, as surgical navigation has been introduced especially in minimally invasive surgery. Localization systems also provide treatments in radiotherapy and radiosurgery with high precision. Virtual or augmented reality plays a role for both surgical training and planning and for safe rehabilitation in the first stage of the recovery from neurological diseases. Also, in the chronic phase of motor diseases, robotics helps with special assistive devices and prostheses. Although, in the past, the actual need and advantage of navigation, localization, and robotics in surgery and therapy has been in doubt, today, the availability of better hardware (e.g., microrobots) and more sophisticated algorithms(e.g., machine learning and other cognitive approaches)has largely increased the field of applications of these technologies,making it more likely that, in the near future, their presence will be dramatically increased, taking advantage of the generational change of the end users and the increasing request of quality in health-care delivery and management.
Learning to Program with Personal Robots: Influences on Student Motivation

Science.gov (United States)

McGill, Monica M.

2012-01-01

One of the goals of using robots in introductory programming courses is to increase motivation among learners. There have been several types of robots that have been used extensively in the classroom to teach a variety of computer science concepts. A more recently introduced robot designed to teach programming to novice students is the Institute…
Ideas from Developmental Robotics and Embodied AI on the Questions of Ethics in Robots

OpenAIRE

Pitti , Alexandre

2017-01-01

Advances in Artificial Intelligence and robotics are currently questioning theethical framework of their applications to deal with potential drifts, as well as the way inwhich these algorithms learn because they will have a strong impact on the behavior ofrobots and the type of robots. interactions with people. We would like to highlight someprinciples and ideas from cognitive neuroscience and development sciences based on theimportance of the body for intelligence, contrary to the theory of ...
Ideas from Developmental Robotics and Embodied AI on the Questions of Ethics in Robots

OpenAIRE

Pitti, Alexandre

2018-01-01

Advances in Artificial Intelligence and robotics are currently questioning theethical framework of their applications to deal with potential drifts, as well as the way inwhich these algorithms learn because they will have a strong impact on the behavior ofrobots and the type of robots. interactions with people. We would like to highlight someprinciples and ideas from cognitive neuroscience and development sciences based on theimportance of the body for intelligence, contrary to the theory of ...
Industrial-Like Vehicle Platforms for Postgraduate Laboratory Courses on Robotics

Science.gov (United States)

Navarro, P. J.; Fernandez, C.; Sanchez, P.

2013-01-01

The interdisciplinary nature of robotics allows mobile robots to be used successfully in a broad range of courses at the postgraduate level and in Ph.D. research. Practical industrial-like mobile robotic demonstrations encourage students and increase their motivation by providing them with learning benefits not achieved with traditional…
Rekreacinių teritorijų kūrimas antropogenizuotoje gamtinėje aplinkoje

OpenAIRE

Šaliamoras, Paulius

2009-01-01

Daugelis kaimo turizmo, rekreacinių teritorijų kūrėjų pateikę specialiai sudarytai komisijai parengtus projektus galėjo gauti nemenką ES paramą, tačiau pasisekė ne visiems, kiti rekreacines teritorijas turėjo rengti už savo lėšas. Vienas tokių projektų yra rekreacinės teritorijos įrengimas Pajaurės pelkėje, esančios 20 km nuo Vilniaus centro. Projektas įdomus tuo, kad rekreacinę teritoriją planuojama įrengti pelkėje, kuri pasižymi silpnu gruntu - durpe, o tai reiškia, kad pelkinį paviršių tek...
Machine Learnig for Robotic Manipulation in cluttered environments

OpenAIRE

Alet Puig, Ferran

2016-01-01

In this thesis we focus on designing the planner for MIT s entry in the Amazon Picking Challenge, a robotic competition aiming at pushing the frontiers of manipulation until robots can substitute human pickers in warehouses. Given a set of manipulation primitives (such as grasping, suction, scooping, placing or pushing) we designed a system capable of learning a planner from a set of manipulation experiments. After learning, given any configuration of objects, the planner can come up with the...
Improving Web Learning through model Optimization using Bootstrap for a Tour-Guide Robot

Directory of Open Access Journals (Sweden)

Rafael León

2012-09-01

Full Text Available We perform a review of Web Mining techniques and we describe a Bootstrap Statistics methodology applied to pattern model classifier optimization and verification for Supervised Learning for Tour-Guide Robot knowledge repository management. It is virtually impossible to test thoroughly Web Page Classifiers and many other Internet Applications with pure empirical data, due to the need for human intervention to generate training sets and test sets. We propose using the computer-based Bootstrap paradigm to design a test environment where they are checked with better reliability
Evolutionary robotics in two decades: A review

Indian Academy of Sciences (India)

Reproduction operator helps in the selection of best candidates in ... through a learning process, for a specific purpose such as robot's control. In neural ... structures. NEAT offers a solution to the problem of competing conventions in a population of ..... service robot: A case study with exhibition visitor flow control. Genetic ...
Educational Robotics: Open Questions and New Challenges

Science.gov (United States)

Alimisis, Dimitris

2013-01-01

This paper investigates the current situation in the field of educational robotics and identifies new challenges and trends focusing on the use of robotic technologies as a tool that will support creativity and other 21st-century learning skills. Finally, conclusions and proposals are presented for promoting cooperation and networking of…
Random neural Q-learning for obstacle avoidance of a mobile robot in unknown environments

Directory of Open Access Journals (Sweden)

Jing Yang

2016-07-01

Full Text Available The article presents a random neural Q-learning strategy for the obstacle avoidance problem of an autonomous mobile robot in unknown environments. In the proposed strategy, two independent modules, namely, avoidance without considering the target and goal-seeking without considering obstacles, are first trained using the proposed random neural Q-learning algorithm to obtain their best control policies. Then, the two trained modules are combined based on a switching function to realize the obstacle avoidance in unknown environments. For the proposed random neural Q-learning algorithm, a single-hidden layer feedforward network is used to approximate the Q-function to estimate the Q-value. The parameters of the single-hidden layer feedforward network are modified using the recently proposed neural algorithm named the online sequential version of extreme learning machine, where the parameters of the hidden nodes are assigned randomly and the sample data can come one by one. However, different from the original online sequential version of extreme learning machine algorithm, the initial output weights are estimated subjected to quadratic inequality constraint to improve the convergence speed. Finally, the simulation results demonstrate that the proposed random neural Q-learning strategy can successfully solve the obstacle avoidance problem. Also, the higher learning efficiency and better generalization ability are achieved by the proposed random neural Q-learning algorithm compared with the Q-learning based on the back-propagation method.
Robot Guided 'Pen Skill' Training in Children with Motor Difficulties.

Science.gov (United States)

Shire, Katy A; Hill, Liam J B; Snapp-Childs, Winona; Bingham, Geoffrey P; Kountouriotis, Georgios K; Barber, Sally; Mon-Williams, Mark

2016-01-01

Motor deficits are linked to a range of negative physical, social and academic consequences. Haptic robotic interventions, based on the principles of sensorimotor learning, have been shown previously to help children with motor problems learn new movements. We therefore examined whether the training benefits of a robotic system would generalise to a standardised test of 'pen-skills', assessed using objective kinematic measures [via the Clinical Kinematic Assessment Tool, CKAT]. A counterbalanced, cross-over design was used in a group of 51 children (37 male, aged 5-11 years) with manual control difficulties. Improved performance on a novel task using the robotic device could be attributed to the intervention but there was no evidence of generalisation to any of the CKAT tasks. The robotic system appears to have the potential to support motor learning, with the technology affording numerous advantages. However, the training regime may need to target particular manual skills (e.g. letter formation) in order to obtain clinically significant improvements in specific skills such as handwriting.
Man-Robot Symbiosis: A Framework For Cooperative Intelligence And Control

Science.gov (United States)

Parker, Lynne E.; Pin, Francois G.

1988-10-01

The man-robot symbiosis concept has the fundamental objective of bridging the gap between fully human-controlled and fully autonomous systems to achieve true man-robot cooperative control and intelligence. Such a system would allow improved speed, accuracy, and efficiency of task execution, while retaining the man in the loop for innovative reasoning and decision-making. The symbiont would have capabilities for supervised and unsupervised learning, allowing an increase of expertise in a wide task domain. This paper describes a robotic system architecture facilitating the symbiotic integration of teleoperative and automated modes of task execution. The architecture reflects a unique blend of many disciplines of artificial intelligence into a working system, including job or mission planning, dynamic task allocation, man-robot communication, automated monitoring, and machine learning. These disciplines are embodied in five major components of the symbiotic framework: the Job Planner, the Dynamic Task Allocator, the Presenter/Interpreter, the Automated Monitor, and the Learning System.
Handling uncertainty and networked structure in robot control

CERN Document Server

Tamás, Levente

2015-01-01

This book focuses on two challenges posed in robot control by the increasing adoption of robots in the everyday human environment: uncertainty and networked communication. Part I of the book describes learning control to address environmental uncertainty. Part II discusses state estimation, active sensing, and complex scenario perception to tackle sensing uncertainty. Part III completes the book with control of networked robots and multi-robot teams. Each chapter features in-depth technical coverage and case studies highlighting the applicability of the techniques, with real robots or in simulation. Platforms include mobile ground, aerial, and underwater robots, as well as humanoid robots and robot arms. Source code and experimental data are available at http://extras.springer.com. The text gathers contributions from academic and industry experts, and offers a valuable resource for researchers or graduate students in robot control and perception. It also benefits researchers in related areas, such as computer...

Learning from Analogies between Robotic World and Natural Phenomena

Science.gov (United States)

Verner, Igor M.; Cuperman, Dan

This paper proposes an approach which combines robotics and science education through the development of robotic models and inquiry into natural phenomena. The robotic models are constructed using the PicoCricket kit. The approach is implemented and evaluated in the framework of teacher training courses for Technion students given in connection with outreach courses for middle school and high school students. The educational study indicated that the proposed approach facilitated acquisition of both technology and science concepts and inspired analogical reasoning and crossdisciplinary connections between the two domains.
Robotic technology results in faster and more robust surgical skill acquisition than traditional laparoscopy.

Science.gov (United States)

Moore, Lee J; Wilson, Mark R; Waine, Elizabeth; Masters, Rich S W; McGrath, John S; Vine, Samuel J

2015-03-01

Technical surgical skills are said to be acquired quicker on a robotic rather than laparoscopic platform. However, research examining this proposition is scarce. Thus, this study aimed to compare the performance and learning curves of novices acquiring skills using a robotic or laparoscopic system, and to examine if any learning advantages were maintained over time and transferred to more difficult and stressful tasks. Forty novice participants were randomly assigned to either a robotic- or laparoscopic-trained group. Following one baseline trial on a ball pick-and-drop task, participants performed 50 learning trials. Participants then completed an immediate retention trial and a transfer trial on a two-instrument rope-threading task. One month later, participants performed a delayed retention trial and a stressful multi-tasking trial. The results revealed that the robotic-trained group completed the ball pick-and-drop task more quickly and accurately than the laparoscopic-trained group across baseline, immediate retention, and delayed retention trials. Furthermore, the robotic-trained group displayed a shorter learning curve for accuracy. The robotic-trained group also performed the more complex rope-threading and stressful multi-tasking transfer trials better. Finally, in the multi-tasking trial, the robotic-trained group made fewer tone counting errors. The results highlight the benefits of using robotic technology for the acquisition of technical surgical skills.
Adaptive heterogeneous multi-robot teams

Energy Technology Data Exchange (ETDEWEB)

Parker, L.E.

1998-11-01

This research addresses the problem of achieving fault tolerant cooperation within small- to medium-sized teams of heterogeneous mobile robots. The author describes a novel behavior-based, fully distributed architecture, called ALLIANCE, that utilizes adaptive action selection to achieve fault tolerant cooperative control in robot missions involving loosely coupled, largely independent tasks. The robots in this architecture possess a variety of high-level functions that they can perform during a mission, and must at all times select an appropriate action based on the requirements of the mission, the activities of other robots, the current environmental conditions, and their own internal states. Since such cooperative teams often work in dynamic and unpredictable environments, the software architecture allows the team members to respond robustly and reliably to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. After presenting ALLIANCE, the author describes in detail the experimental results of an implementation of this architecture on a team of physical mobile robots performing a cooperative box pushing demonstration. These experiments illustrate the ability of ALLIANCE to achieve adaptive, fault-tolerant cooperative control amidst dynamic changes in the capabilities of the robot team.
Laparoscopy-assisted Robotic Myomectomy Using the DA Vinci System

Directory of Open Access Journals (Sweden)

Shih-Peng Mao

2007-06-01

Conclusion: Minimally invasive surgery is the trend of the future. Robot-assisted laparoscopic surgery is a new technique for myomectomy. This robotic system provides a three-dimensional operative field and an easy-to-use control panel, which may be of great help when applying the suturing techniques and may shorten the learning curve. More experience with and long-term follow-up of robotic surgery may be warranted to further validate the role the robot-assisted approach in gynecologic surgery.
Passive mapping and intermittent exploration for mobile robots

Science.gov (United States)

Engleson, Sean P.

1994-01-01

An adaptive state space architecture is combined with diktiometric representation to provide the framework for designing a robot mapping system with flexible navigation planning tasks. This involves indexing waypoints described as expectations, geometric indexing, and perceptual indexing. Matching and updating the robot's projected position and sensory inputs with indexing waypoints involves matchers, dynamic priorities, transients, and waypoint restructuring. The robot's map learning can be opganized around the principles of passive mapping.
Robotics Competitions: The Choice Is up to You!

Science.gov (United States)

Johnson, Richard T.; Londt, Susan E.

2010-01-01

Competitive robotics as an interactive experience can increase the level of student participation in technology education, inspire students to consider careers in technical fields, and enhance the visibility of technology education programs. Implemented correctly, a competitive robotics program can provide a stimulating learning environment for…
[Individual learning curve for radical robot-assisted prostatectomy based on the example of three professionals working in one clinic].

Science.gov (United States)

Rasner, P I; Pushkar', D Iu; Kolontarev, K B; Kotenkov, D V

2014-01-01

The appearance of new surgical technique always requires evaluation of its effectiveness and ease of acquisition. A comparative study of the results of the first three series of successive robot-assisted radical prostatectomy (RARP) performed on at time by three surgeons, was conducted. The series consisted of 40 procedures, and were divided into 4 groups of 10 operations for the analysis. When comparing data, statistically significant improvement of intra- and postoperative performance in each series was revealed, with increase in the number of operations performed, and in each subsequent series compared with the preceding one. We recommend to perform the planned conversion at the first operation. In our study, previous laparoscopic experience did not provide any significant advantages in the acquisition of robot-assisted technology. To characterize the individual learning curve, we recommend the use of the number of operations that the surgeon looked in the life-surgery regimen and/or in which he participated as an assistant before his own surgical activity, as well as the indicator "technical defect". In addition to the term "individual learning curve", we propose to introduce the terms "surgeon's individual training phase", and "clinic's learning curve".
LEGO-based Robotics in Higher Education: 15 Years of Student Creativity

Directory of Open Access Journals (Sweden)

Ethan Danahy

2014-02-01

Full Text Available Our goal in this article is to reflect on the role LEGO robotics has played in college engineering education over the last 15 years, starting with the introduction of the RCX in 1998 and ending with the introduction of the EV3 in 2013. By combining a modular computer programming language with a modular building platform, LEGO Education has allowed students (of all ages to become active leaders in their own education as they build everything from animals for a robotic zoo to robots that play children's games. Most importantly, it allows all students to develop different solutions to the same problem to provide a learning community. We look first at how the recent developments in the learning sciences can help in promoting student learning in robotics. We then share four case studies of successful college-level implementations that build on these developments.
Using Multi-Robot Systems for Engineering Education: Teaching and Outreach with Large Numbers of an Advanced, Low-Cost Robot

Science.gov (United States)

McLurkin, J.; Rykowski, J.; John, M.; Kaseman, Q.; Lynch, A. J.

2013-01-01

This paper describes the experiences of using an advanced, low-cost robot in science, technology, engineering, and mathematics (STEM) education. It presents three innovations: It is a powerful, cheap, robust, and small advanced personal robot; it forms the foundation of a problem-based learning curriculum; and it enables a novel multi-robot…
Bij bijna alles om je heen is TNO betrokken

NARCIS (Netherlands)

Schijndel-de Nooij, M. van

2010-01-01

Als iemand ir. Margriet van Schijndel vijf jaar geleden had verteld dat zij nu zou werken bij TNO Automotive, zou zij hem niet hebben geloofd. Nu werkt zij er met zeer veel plezier. ‘Het prettige van TNO is dat het een zeer brede organisatie is waarbinnen je zelf kunt ontdekken welk vakgebied of
Can robotic surgery be done efficiently while training residents?

Science.gov (United States)

Honaker, Michael Drew; Paton, Beverly L; Stefanidis, Dimitrios; Schiffern, Lynnette M

2015-01-01

Robotic surgery is a rapidly growing area in surgery. In an era of emphasis on cost reduction, the question becomes how do you train residents in robotic surgery? The aim of this study was to determine if there was a difference in operative time and complications when comparing general surgery residents learning robotic cholecystectomies to those learning standard laparoscopic cholecystectomies. A retrospective analysis of adult patients undergoing robotic and laparoscopic cholecystectomy by surgical residents between March 2013 and February 2014 was conducted. Demographic data, operative factors, length of stay (LOS), and complications were examined. Univariate and multivariate analyses were performed. The significance was set at p robotic cholecystectomy group and 40 in the laparoscopic group). Age, diagnosis, and American Society of Anesthesiologists score were not significantly different between groups. There was only 1 complication in the standard laparoscopic group in which a patient had to be taken back to surgery because of an incarcerated port site. LOS was significantly higher in the standard laparoscopic group (mean = 2.28) than in the robotic group (mean = 0.56; p robotic group (mean = 97.00 minutes; p = 0.4455). When intraoperative cholangiogram was evaluated, OR time was shorter in the robotic group. Robotic training in general surgery residency does not amount to extra OR time. LOS in our study was significantly longer in the standard laparoscopic group. Copyright © 2015 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.
Liability exposure for surgical robotics instructors.

Science.gov (United States)

Lee, Yu L; Kilic, Gokhan; Phelps, John Y

2012-01-01

Surgical robotics instructors provide an essential service in improving the competency of novice gynecologic surgeons learning robotic surgery and advancing surgical skills on behalf of patients. However, despite best intentions, robotics instructors and the gynecologists who use their services expose themselves to liability. The fear of litigation in the event of a surgical complication may reduce the availability and utility of robotics instructors. A better understanding of the principles of duty of care and the physician-patient relationship, and their potential applicability in a court of law likely will help to dismantle some concerns and uncertainties about liability. This commentary is not meant to discourage current and future surgical instructors but to raise awareness of liability issues among robotics instructors and their students and to recommend certain preventive measures to curb potential liability risks. Published by Elsevier Inc.
Field Tested Service Oriented Robotic Architecture: Case Study

Science.gov (United States)

Flueckiger, Lorenzo; Utz, Hanz

2012-01-01

This paper presents the lessons learned from six years of experiments with planetary rover prototypes running the Service Oriented Robotic Architecture (SORA) developed by the Intelligent Robotics Group (IRG) at NASA Ames Research Center. SORA relies on proven software methods and technologies applied to the robotic world. Based on a Service Oriented Architecture and robust middleware, SORA extends its reach beyond the on-board robot controller and supports the full suite of software tools used during mission scenarios from ground control to remote robotic sites. SORA has been field tested in numerous scenarios of robotic lunar and planetary exploration. The results of these high fidelity experiments are illustrated through concrete examples that have shown the benefits of using SORA as well as its limitations.
Developing Humanoid Robots for Real-World Environments

Science.gov (United States)

Stoica, Adrian; Kuhlman, Michael; Assad, Chris; Keymeulen, Didier

2008-01-01

Humanoids are steadily improving in appearance and functionality demonstrated in controlled environments. To address the challenges of operation in the real-world, researchers have proposed the use of brain-inspired architectures for robot control, and the use of robot learning techniques that enable the robot to acquire and tune skills and behaviours. In the first part of the paper we introduce new concepts and results in these two areas. First, we present a cerebellum-inspired model that demonstrated efficiency in the sensory-motor control of anthropomorphic arms, and in gait control of dynamic walkers. Then, we present a set of new ideas related to robot learning, emphasizing the importance of developing teaching techniques that support learning. In the second part of the paper we propose the use in robotics of the iterative and incremental development methodologies, in the context of practical task-oriented applications. These methodologies promise to rapidly reach system-level integration, and to early identify system-level weaknesses to focus on. We apply this methodology in a task targeting the automated assembly of a modular structure using HOAP-2. We confirm this approach led to rapid development of a end-to-end capability, and offered guidance on which technologies to focus on for gradual improvement of a complete functional system. It is believed that providing Grand Challenge type milestones in practical task-oriented applications accelerates development. As a meaningful target in short-mid term we propose the 'IKEA Challenge', aimed at the demonstration of autonomous assembly of various pieces of furniture, from the box, following included written/drawn instructions.
Social Intelligence for a Robot Engaging People in Cognitive Training Activities

Directory of Open Access Journals (Sweden)

Jeanie Chan

2012-10-01

Full Text Available Current research supports the use of cognitive training interventions to improve the brain functioning of both adults and children. Our work focuses on exploring the potential use of robot assistants to allow for these interventions to become more accessible. Namely, we aim to develop an intelligent, socially assistive robot that can engage individuals in person-centred cognitively stimulating activities. In this paper, we present the design of a novel control architecture for the robot Brian 2.0, which enables the robot to be a social motivator by providing assistance, encouragement and celebration during an activity. A hierarchical reinforcement learning approach is used in the architecture to allow the robot to: 1 learn appropriate assistive behaviours based on the structure of the activity, and 2 personalize an interaction based on user states. Experiments show that the control architecture is effective in determining the robot's optimal assistive behaviours during a memory game interaction.
Swarm robotics and minimalism

Science.gov (United States)

Sharkey, Amanda J. C.

2007-09-01

Swarm Robotics (SR) is closely related to Swarm Intelligence, and both were initially inspired by studies of social insects. Their guiding principles are based on their biological inspiration and take the form of an emphasis on decentralized local control and communication. Earlier studies went a step further in emphasizing the use of simple reactive robots that only communicate indirectly through the environment. More recently SR studies have moved beyond these constraints to explore the use of non-reactive robots that communicate directly, and that can learn and represent their environment. There is no clear agreement in the literature about how far such extensions of the original principles could go. Should there be any limitations on the individual abilities of the robots used in SR studies? Should knowledge of the capabilities of social insects lead to constraints on the capabilities of individual robots in SR studies? There is a lack of explicit discussion of such questions, and researchers have adopted a variety of constraints for a variety of reasons. A simple taxonomy of swarm robotics is presented here with the aim of addressing and clarifying these questions. The taxonomy distinguishes subareas of SR based on the emphases and justifications for minimalism and individual simplicity.
People-Centered Development of a Smart Learning Ecosystem of Adaptive Robots

DEFF Research Database (Denmark)

Fischer, Daniel Kjær Bonde; Kristiansen, Jakob; Mariager, Casper

2019-01-01

Robots are currently moving out of the laboratory and company floor into more human and social contexts including care, rehabilitation and education. While those robots are usually envisioned as a kind of social interaction partner, we suggest a different approach, where robots become adaptive...
Navigating the pathway to robotic competency in general thoracic surgery.

Science.gov (United States)

Seder, Christopher W; Cassivi, Stephen D; Wigle, Dennis A

2013-01-01

Although robotic technology has addressed many of the limitations of traditional videoscopic surgery, robotic surgery has not gained widespread acceptance in the general thoracic community. We report our initial robotic surgery experience and propose a structured, competency-based pathway for the development of robotic skills. Between December 2008 and February 2012, a total of 79 robot-assisted pulmonary, mediastinal, benign esophageal, or diaphragmatic procedures were performed. Data on patient characteristics and perioperative outcomes were retrospectively collected and analyzed. During the study period, one surgeon and three residents participated in a triphasic, competency-based pathway designed to teach robotic skills. The pathway consisted of individual preclinical learning followed by mentored preclinical exercises and progressive clinical responsibility. The robot-assisted procedures performed included lung resection (n = 38), mediastinal mass resection (n = 19), hiatal or paraesophageal hernia repair (n = 12), and Heller myotomy (n = 7), among others (n = 3). There were no perioperative mortalities, with a 20% complication rate and a 3% readmission rate. Conversion to a thoracoscopic or open approach was required in eight pulmonary resections to facilitate dissection (six) or to control hemorrhage (two). Fewer major perioperative complications were observed in the later half of the experience. All residents who participated in the thoracic surgery robotic pathway perform robot-assisted procedures as part of their clinical practice. Robot-assisted thoracic surgery can be safely learned when skill acquisition is guided by a structured, competency-based pathway.
Architecture for Multiple Interacting Robot Intelligences

Science.gov (United States)

Peters, Richard Alan, II (Inventor)

2008-01-01

An architecture for robot intelligence enables a robot to learn new behaviors and create new behavior sequences autonomously and interact with a dynamically changing environment. Sensory information is mapped onto a Sensory Ego-Sphere (SES) that rapidly identifies important changes in the environment and functions much like short term memory. Behaviors are stored in a database associative memory (DBAM) that creates an active map from the robot's current state to a goal state and functions much like long term memory. A dream state converts recent activities stored in the SES and creates or modifies behaviors in the DBAM.
Integrated Robotic systems for Humanitarian Demining

Directory of Open Access Journals (Sweden)

E. Colon

2007-06-01

Full Text Available This paper summarises the main results of 10 years of research and development in Humanitarian Demining. The Hudem project focuses on mine detection systems and aims at provided different solutions to support the mine detection operations. Robots using different kind of locomotion systems have been designed and tested on dummy minefields. In order to control these robots, software interfaces, control algorithms, visual positioning and terrain following systems have also been developed. Typical data acquisition results obtained during trial campaigns with robots and data acquisition systems are reported. Lessons learned during the project and future work conclude this paper.

Chimeric classical swine fever (CSF)-Japanese encephalitis (JE) viral particles as a non-transmissible bivalent marker vaccine candidate against CSF and JE infections

Science.gov (United States)

A trans-complemented CSF- JE chimeric viral replicon was constructed using an infectious cDNA clone of the CSF virus (CSFV) Alfort/187 strain. The E2 gene of CSFV Alfort/187 strain was deleted and the resultant plasmid pA187delE2 was inserted by a fragment containing the region coding for a truncate...
Exploration and Navigation for Mobile Robots With Perceptual Limitations

Directory of Open Access Journals (Sweden)

Leonardo Romero

2006-09-01

Full Text Available To learn a map of an environment a mobile robot has to explore its workspace using its sensors. Sensors are noisy and have perceptual limitations that must be considered while learning a map. This paper considers a mobile robot with sensor perceptual limitations and introduces a new method for exploring and navigating autonomously in indoor environments. To minimize the risk of collisions as well as to not exceed the range of sensors, we introduce the concept of a travel space as a way to associate costs to grid cells of the map, based on distances to obstacles. During exploration the mobile robot minimizes its movements, including rotations, to reach the nearest unexplored region of the environment, using a dynamic programming algorithm. Once the exploration ends, the travel space is used to form a roadmap, a net of safe roads that the mobile robot can use for navigation. These exploration and navigation method are tested using a simulated and a real mobile robot with promising results.
Exploration and Navigation for Mobile Robots With Perceptual Limitations

Directory of Open Access Journals (Sweden)

Eduardo F. Morales

2008-11-01

Full Text Available To learn a map of an environment a mobile robot has to explore its workspace using its sensors. Sensors are noisy and have perceptual limitations that must be considered while learning a map. This paper considers a mobile robot with sensor perceptual limitations and introduces a new method for exploring and navigating autonomously in indoor environments. To minimize the risk of collisions as well as to not exceed the range of sensors, we introduce the concept of a travel space as a way to associate costs to grid cells of the map, based on distances to obstacles. During exploration the mobile robot minimizes its movements, including rotations, to reach the nearest unexplored region of the environment, using a dynamic programming algorithm. Once the exploration ends, the travel space is used to form a roadmap, a net of safe roads that the mobile robot can use for navigation. These exploration and navigation method are tested using a simulated and a real mobile robot with promising results.
Using expectations to monitor robotic progress and recover from problems

Science.gov (United States)

Kurup, Unmesh; Lebiere, Christian; Stentz, Anthony; Hebert, Martial

2013-05-01

How does a robot know when something goes wrong? Our research answers this question by leveraging expectations - predictions about the immediate future - and using the mismatch between the expectations and the external world to monitor the robot's progress. We use the cognitive architecture ACT-R (Adaptive Control of Thought - Rational) to learn the associations between the current state of the robot and the world, the action to be performed in the world, and the future state of the world. These associations are used to generate expectations that are then matched by the architecture with the next state of the world. A significant mismatch between these expectations and the actual state of the world indicate a problem possibly resulting from unexpected consequences of the robot's actions, unforeseen changes in the environment or unanticipated actions of other agents. When a problem is detected, the recovery model can suggest a number of recovery options. If the situation is unknown, that is, the mismatch between expectations and the world is novel, the robot can use a recovery solution from a set of heuristic options. When a recovery option is successfully applied, the robot learns to associate that recovery option with the mismatch. When the same problem is encountered later, the robot can apply the learned recovery solution rather than using the heuristics or randomly exploring the space of recovery solutions. We present results from execution monitoring and recovery performed during an assessment conducted at the Combined Arms Collective Training Facility (CACTF) at Fort Indiantown Gap.
SLAM algorithm applied to robotics assistance for navigation in unknown environments

Directory of Open Access Journals (Sweden)

Lobo Pereira Fernando

2010-02-01

Full Text Available Abstract Background The combination of robotic tools with assistance technology determines a slightly explored area of applications and advantages for disability or elder people in their daily tasks. Autonomous motorized wheelchair navigation inside an environment, behaviour based control of orthopaedic arms or user's preference learning from a friendly interface are some examples of this new field. In this paper, a Simultaneous Localization and Mapping (SLAM algorithm is implemented to allow the environmental learning by a mobile robot while its navigation is governed by electromyographic signals. The entire system is part autonomous and part user-decision dependent (semi-autonomous. The environmental learning executed by the SLAM algorithm and the low level behaviour-based reactions of the mobile robot are robotic autonomous tasks, whereas the mobile robot navigation inside an environment is commanded by a Muscle-Computer Interface (MCI. Methods In this paper, a sequential Extended Kalman Filter (EKF feature-based SLAM algorithm is implemented. The features correspond to lines and corners -concave and convex- of the environment. From the SLAM architecture, a global metric map of the environment is derived. The electromyographic signals that command the robot's movements can be adapted to the patient's disabilities. For mobile robot navigation purposes, five commands were obtained from the MCI: turn to the left, turn to the right, stop, start and exit. A kinematic controller to control the mobile robot was implemented. A low level behavior strategy was also implemented to avoid robot's collisions with the environment and moving agents. Results The entire system was tested in a population of seven volunteers: three elder, two below-elbow amputees and two young normally limbed patients. The experiments were performed within a closed low dynamic environment. Subjects took an average time of 35 minutes to navigate the environment and to learn how
Force feedback facilitates multisensory integration during robotic tool use

NARCIS (Netherlands)

Sengül, A.; Rognini, G.; van Elk, M.; Aspell, J.E.; Bleuler, H.; Blanke, O.

2013-01-01

The present study investigated the effects of force feedback in relation to tool use on the multisensory integration of visuo-tactile information. Participants learned to control a robotic tool through a surgical robotic interface. Following tool-use training, participants performed a crossmodal
Teachers' perceptions of the benefits and the challenges of integrating educational robots into primary/elementary curricula

Science.gov (United States)

Khanlari, Ahmad

2016-05-01

Twenty-first century education systems should create an environment wherein students encounter critical learning components (such as problem-solving, teamwork, and communication skills) and embrace lifelong learning. A review of literature demonstrates that new technologies, in general, and robotics, in particular, are well suited for this aim. This study aims to contribute to the literature by studying teachers' perceptions of the effects of using robotics on students' lifelong learning skills. This study also seeks to better understand teachers' perceptions of the barriers of using robotics and the support they need. Eleven primary/elementary teachers from Newfoundland and Labrador English Schools District participated in this study. The results of this study revealed that robotics is perceived by teachers to have positive effects on students' lifelong learning skills. Furthermore, the participants indicated a number of barriers to integrate robotics into their teaching activities and expressed the support they need.
Robot Guided 'Pen Skill' Training in Children with Motor Difficulties.

Directory of Open Access Journals (Sweden)

Katy A Shire

Full Text Available Motor deficits are linked to a range of negative physical, social and academic consequences. Haptic robotic interventions, based on the principles of sensorimotor learning, have been shown previously to help children with motor problems learn new movements. We therefore examined whether the training benefits of a robotic system would generalise to a standardised test of 'pen-skills', assessed using objective kinematic measures [via the Clinical Kinematic Assessment Tool, CKAT]. A counterbalanced, cross-over design was used in a group of 51 children (37 male, aged 5-11 years with manual control difficulties. Improved performance on a novel task using the robotic device could be attributed to the intervention but there was no evidence of generalisation to any of the CKAT tasks. The robotic system appears to have the potential to support motor learning, with the technology affording numerous advantages. However, the training regime may need to target particular manual skills (e.g. letter formation in order to obtain clinically significant improvements in specific skills such as handwriting.
Learning to Explain: The Role of Educational Robots in Science Education

Science.gov (United States)

Datteri, Edoardo; Zecca, Luisa; Laudisa, Federico; Castiglioni, Marco

2013-01-01

Educational robotics laboratories typically involve building and programming robotic systems to perform particular tasks or solve problems. In this paper we explore the potential educational value of a form of robot-supported educational activity that has been little discussed in the literature. During these activities, primary school children are…
A curious robot: An explorative-exploitive inference algorithm

DEFF Research Database (Denmark)

Pedersen, Kim Steenstrup; Johansen, Peter

2007-01-01

We propose a sequential learning algorithm with a focus on robot control. It is initialised by a teacher who directs the robot through a series of example solutions of a problem. Left alone, the control chooses its next action by prediction based on a variable order Markov chain model selected to...
Je brein de baas : Over de rol van bewust denken

NARCIS (Netherlands)

Aleman, (Andreas)

2017-01-01

In Je brein de baas neemt neuropsycholoog André Aleman het op voor het bewuste denken. Onderzoek heeft eerder aangetoond dat juist onbewuste processen in ons brein een grote invloed kunnen hebben op ons gedrag. Daardoor zijn veel wetenschappers de rol van bewust denken gaan bagatelliseren: het heeft
Sensor based real-time control of robots

DEFF Research Database (Denmark)

Andersen, Thomas Timm

in the sensor to actuation delays in the robot. To that end a method for measuring the actuation and response delay of an industrial robot manipulator, relative to the joint configuration of the robot, is presented. It is also shown how modern machine learning algorithms can be trained to build model based......As robots are becoming more and more widespread in manufacturing, the desire and need for more advanced robotic solutions are increasingly expressed. This is especially the case in Denmark where products with natural variances like agricultural products takes up a large share of the produced goods....... For such production lines, it is often not possible to use primitive preprogrammed industrial robots to handle the otherwise repetitive tasks due to the uniqueness of each product. To handle such products it is necessary to use sensors to determine the size, shape, and position of the product before a proper...
Apskaitos politikos formavimas įmonėje

OpenAIRE

Pelurytytė, Elinga

2007-01-01

Tyrimo objektas – įmonių finansinės apskaitos politika. Tyrimo dalykas – finansinės apskaitos politikos formavimas įmonėje. Darbo tikslas – nustačius apskaitos politikos formavimo įmonėse ypatumus, parengti teorinį įmonės finansinės apskaitos politikos formavimo modelį ir patikrinus jo praktinį pritaikomumą Lietuvos įmonėse, suformuluoti atitinkamas išvadas bei pateikti pasiūlymus apskaitos politikos formavimo metodikai tobulinti. Uždaviniai: 1) ištirti apskaitos politikos regla...
Learning Direction of Attention for a Social Robot in Noisy Environments

DEFF Research Database (Denmark)

Thomsen, Nicolai Bæk; Tan, Zheng-Hua; Lindberg, Børge

2015-01-01

It is essential for social robots to be able to locate and direct attention towards communicating persons, however the operating environments can be challenging. When using sound source localization (SSL) acoustic noise sources can distract the robot, which interrupts the desired interaction...... with people. Since the noise sources can be of many different kinds, it is important for the robot to adapt to any environment. In this paper we present a simple strategy for a robot to adapt to the environment using feedback from a face detection routine, thus eventually only directing attention towards...
Bio-robots automatic navigation with electrical reward stimulation.

Science.gov (United States)

Sun, Chao; Zhang, Xinlu; Zheng, Nenggan; Chen, Weidong; Zheng, Xiaoxiang

2012-01-01

Bio-robots that controlled by outer stimulation through brain computer interface (BCI) suffer from the dependence on realtime guidance of human operators. Current automatic navigation methods for bio-robots focus on the controlling rules to force animals to obey man-made commands, with animals' intelligence ignored. This paper proposes a new method to realize the automatic navigation for bio-robots with electrical micro-stimulation as real-time rewards. Due to the reward-seeking instinct and trial-and-error capability, bio-robot can be steered to keep walking along the right route with rewards and correct its direction spontaneously when rewards are deprived. In navigation experiments, rat-robots learn the controlling methods in short time. The results show that our method simplifies the controlling logic and realizes the automatic navigation for rat-robots successfully. Our work might have significant implication for the further development of bio-robots with hybrid intelligence.
[Robots in general surgery: present and future].

Science.gov (United States)

Galvani, Carlos; Horgan, Santiago

2005-09-01

Robotic surgery is an emerging technology. We began to use this technique in 2000, after it was approved by the Food and Drug Administration. Our preliminary experience was satisfactory. We report 4 years' experience of using this technique in our institution. Between August 2000 and December 2004, 399 patients underwent robotic surgery using the Da Vinci system. We performed 110 gastric bypass procedures, 30 Lap band, 59 Heller myotomies, 12 Nissen fundoplications, 6 epiphrenic diverticula, 18 total esophagectomies, 3 esophageal leiomyoma resections, 1 pyloroplasty, 2 gastrojejunostomies, 2 transduodenal sphincteroplasties, 10 adrenalectomies and 145 living-related donor nephrectomies. Operating times for fundoplications and Lap band were longer. After the learning curve, the operating times and morbidity of the remaining procedures were considerably reduced. Robot-assisted surgery allows advanced laparoscopic procedures to be performed with enhanced results given that it reduces the learning curve as measured by operating time and morbidity.
Robotic Literacy Learning Companions: Exploring Student Engagement with a Humanoid Robot in an Afterschool Literacy Program

Science.gov (United States)

Levchak, Sofia

2016-01-01

This study was an investigation of the use of a NAO humanoid robot as an effective tool for engaging readers in an afterschool program as well as to find if increasing engagement using a humanoid robot would affect students' reading comprehension when compared to traditional forms of instruction. The targeted population of this study was…
Exploring the acquisition and production of grammatical constructions through human-robot interaction with echo state networks.

Science.gov (United States)

Hinaut, Xavier; Petit, Maxime; Pointeau, Gregoire; Dominey, Peter Ford

2014-01-01

One of the principal functions of human language is to allow people to coordinate joint action. This includes the description of events, requests for action, and their organization in time. A crucial component of language acquisition is learning the grammatical structures that allow the expression of such complex meaning related to physical events. The current research investigates the learning of grammatical constructions and their temporal organization in the context of human-robot physical interaction with the embodied sensorimotor humanoid platform, the iCub. We demonstrate three noteworthy phenomena. First, a recurrent network model is used in conjunction with this robotic platform to learn the mappings between grammatical forms and predicate-argument representations of meanings related to events, and the robot's execution of these events in time. Second, this learning mechanism functions in the inverse sense, i.e., in a language production mode, where rather than executing commanded actions, the robot will describe the results of human generated actions. Finally, we collect data from naïve subjects who interact with the robot via spoken language, and demonstrate significant learning and generalization results. This allows us to conclude that such a neural language learning system not only helps to characterize and understand some aspects of human language acquisition, but also that it can be useful in adaptive human-robot interaction.
A Preliminary Study Exploring the Use of Fictional Narrative in Robotics Activities

Science.gov (United States)

Williams, Douglas; Ma, Yuxin; Prejean, Louise

2010-01-01

Educational robotics activities are gaining in popularity. Though some research data suggest that educational robotics can be an effective approach in teaching mathematics, science, and engineering, research is needed to generate the best practices and strategies for designing these learning environments. Existing robotics activities typically do…
Robotics in general thoracic surgery procedures.

Science.gov (United States)

Latif, M Jawad; Park, Bernard J

2017-01-01

The use of robotic technology in general thoracic surgical practice continues to expand across various institutions and at this point many major common thoracic surgical procedures have been successfully performed by general thoracic surgeons using the robotic technology. These procedures include lung resections, excision of mediastinal masses, esophagectomy and reconstruction for malignant and benign esophageal pathologies. The success of robotic technology can be attributed to highly magnified 3-D visualization, dexterity afforded by 7 degrees of freedom that allow difficult dissections in narrow fields and the ease of reproducibility once the initial set up and instruments become familiar to the surgeon. As the application of robotic technology trickle downs from major academic centers to community hospitals, it becomes imperative that its role, limitations, learning curve and financial impact are understood by the novice robotic surgeon. In this article, we share our experience as it relates to the setup, common pitfalls and long term results for more commonly performed robotic assisted lung and thymic resections using the 4 arm da Vinci Xi robotic platform (Intuitive Surgical, Inc., Sunnyvale, CA, USA) to help guide those who are interested in adopting this technology.

Robotic laparoscopic surgery: cost and training.

Science.gov (United States)

Amodeo, A; Linares Quevedo, A; Joseph, J V; Belgrano, E; Patel, H R H

2009-06-01

The advantages of minimally invasive surgery are well accepted. Shorter hospital stays, decreased postoperative pain, rapid return to preoperative activity, decreased postoperative ileus, and preserved immune function are among the benefits of the laparoscopic approach. However, the instruments of laparoscopy afford surgeons limited precision and poor ergonomics, and their use is associated with a significant learning curve and the amount of time and energy necessary to develop and maintain such advanced laparoscopic skills is not insignificant. The robotic surgery allows all laparoscopists to perform advanced laparoscopic procedures with greater ease. The potential advantages of surgical robotic systems include making advanced laparoscopic surgical procedures accessible to surgeons who do not have advanced video endoscopic training and broadening the scope of surgical procedures that can be performed using the laparoscopic method. The wristed instruments, x10 magnifications, tremor filtering, scaling of movements and three-dimensional view allow the urologist to perform the intricate dissection and anastomosis with high precision. The robot is not, however, without significant disadvantages as compared with traditional laparoscopy. These include greater expense and consumption of operating room resources such as space and the availability of skilled technical staff, complete elimination of tactile feedback, and more limited options for trocar placement. The current cost of the da Vinci system is $ 1.2 million and annual maintenance is $ 138000. Many studies suggest that depreciation and maintenance costs can be minimised if the number of robotic cases is increased. The high cost of purchasing and maintaining the instruments of the robotic system is one of its many disadvantages. The availability of the robotic systems to only a limited number of centres reduces surgical training opportunities. Hospital administrators and surgeons must define the reasons for
Biologically-Inspired Control Architecture for Musical Performance Robots

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Jorge Solis

2014-10-01

Full Text Available At Waseda University, since 1990, the authors have been developing anthropomorphic musical performance robots as a means for understanding human control, introducing novel ways of interaction between musical partners and robots, and proposing applications for humanoid robots. In this paper, the design of a biologically-inspired control architecture for both an anthropomorphic flutist robot and a saxophone playing robot are described. As for the flutist robot, the authors have focused on implementing an auditory feedback system to improve the calibration procedure for the robot in order to play all the notes correctly during a performance. In particular, the proposed auditory feedback system is composed of three main modules: an Expressive Music Generator, a Feed Forward Air Pressure Control System and a Pitch Evaluation System. As for the saxophone-playing robot, a pressure-pitch controller (based on the feedback error learning to improve the sound produced by the robot during a musical performance was proposed and implemented. In both cases studied, a set of experiments are described to verify the improvements achieved while considering biologically-inspired control approaches.
If I had a robot it should do Everything for me

DEFF Research Database (Denmark)

Bjørner, Thomas

2009-01-01

This paper presents evaluation research studying Danish children's attitudes towards robotic technologies. The purpose of the paper is to focus on children's contextualisation and evaluation of different robotic technologies and discuss an approach combining contextual and technical issues...... in a learning context. It appears from this study that children know pretty much about robots, and they distinguish between different kinds of robots, and their attitudes towards robots is rather complex. It seems that children evaluate robots from four elements of technology, which consist of technical...... children positive attitudes towards science and technology, including robots. The other group of children did not participate in FLL....
Teaching Functional Patterns through Robotic Applications

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J. Boender

2016-11-01

Full Text Available We present our approach to teaching functional programming to First Year Computer Science students at Middlesex University through projects in robotics. A holistic approach is taken to the curriculum, emphasising the connections between different subject areas. A key part of the students' learning is through practical projects that draw upon and integrate the taught material. To support these, we developed the Middlesex Robotic plaTfOrm (MIRTO, an open-source platform built using Raspberry Pi, Arduino, HUB-ee wheels and running Racket (a LISP dialect. In this paper we present the motivations for our choices and explain how a number of concepts of functional programming may be employed when programming robotic applications. We present some students' work with robotics projects: we consider the use of robotics projects to have been a success, both for their value in reinforcing students' understanding of programming concepts and for their value in motivating the students.
The universal robot

Science.gov (United States)

Moravec, Hans

1993-12-01

Our artifacts are getting smarter, and a loose parallel with the evolution of animal intelligence suggests one future course for them. Computerless industrial machinery exhibits the behavioral flexibility of single-celled organisms. Today's best computer-controlled robots are like the simpler invertebrates. A thousand-fold increase in computer power in the next decade should make possible machines with reptile-like sensory and motor competence. Properly configured, such robots could do in the physical world what personal computers now do in the world of data - act on our behalf as literal-minded slaves. Growing computer power over the next half-century will allow this reptile stage to be surpassed, in stages producing robots that learn like mammals, model their world like primates, and eventually reason like humans. Depending on your point of view, humanity will then have produced a worthy successor, or transcended some of its inherited limitations and so transformed itself into something quite new.
Embodying a cognitive model in a mobile robot

Science.gov (United States)

Benjamin, D. Paul; Lyons, Damian; Lonsdale, Deryle

2006-10-01

The ADAPT project is a collaboration of researchers in robotics, linguistics and artificial intelligence at three universities to create a cognitive architecture specifically designed to be embodied in a mobile robot. There are major respects in which existing cognitive architectures are inadequate for robot cognition. In particular, they lack support for true concurrency and for active perception. ADAPT addresses these deficiencies by modeling the world as a network of concurrent schemas, and modeling perception as problem solving. Schemas are represented using the RS (Robot Schemas) language, and are activated by spreading activation. RS provides a powerful language for distributed control of concurrent processes. Also, The formal semantics of RS provides the basis for the semantics of ADAPT's use of natural language. We have implemented the RS language in Soar, a mature cognitive architecture originally developed at CMU and used at a number of universities and companies. Soar's subgoaling and learning capabilities enable ADAPT to manage the complexity of its environment and to learn new schemas from experience. We describe the issues faced in developing an embodied cognitive architecture, and our implementation choices.
Japanese Encephalitis Virus Infection Results in Transient Dysfunction of Memory Learning and Cholinesterase Inhibition.

Science.gov (United States)

Chauhan, Prashant Singh; Khanna, Vinay Kumar; Kalita, Jayantee; Misra, Usha Kant

2017-08-01

Cholinergic system has an important role in memory and learning. Abnormal cognitive and behavioral changes have been reported in Japanese encephalitis (JE), but their basis has not been comprehensively evaluated. In this study, we report memory and learning and its association with acetylcholinesterase (AChE) activity, JE virus titer, and with histopathological observations in a rat model of JE. Wistar rats were intracerebrally inoculated on 12th day with 3 × 10 6 pfu/ml of JE virus. Memory and learning were assessed by the active and passive avoidance tests on 10, 33, and 48 days post inoculation (dpi). After 10, 33, and 48 dpi AChE activity, Japanese encephalitis virus (JEV) titer and histopathological changes were studied in the frontal cortex, thalamus, midbrain, cerebellum, and hippocampus. There was significant impairment in memory and learning on 10 dpi which started improving from 33 dpi to 48 dpi by active avoidance test. Passive avoidance test showed decrease in transfer latency time of retention trial compared to acquisition on first, second, and third retention day trial compared to controls. AChE inhibition was more marked in the hippocampus, frontal cortex, and cerebellum on 10 dpi. However, AChE activity started improving from 33 dpi to 48 dpi. AChE activity in the thalamus and midbrain correlated with active avoidance test on 10 dpi and 33 dpi. Histopathological studies also revealed improvement on 33 and 48 compared to 10 dpi. The present study demonstrates transient memory and learning impairment which was associated with reduction in AChE, JEV titer, and damage in different brain regions of JEV infected rats.
Can a virtual reality surgical simulation training provide a self-driven and mentor-free skills learning? Investigation of the practical influence of the performance metrics from the virtual reality robotic surgery simulator on the skill learning and associated cognitive workloads.

Science.gov (United States)

Lee, Gyusung I; Lee, Mija R

2018-01-01

While it is often claimed that virtual reality (VR) training system can offer self-directed and mentor-free skill learning using the system's performance metrics (PM), no studies have yet provided evidence-based confirmation. This experimental study investigated what extent to which trainees achieved their self-learning with a current VR simulator and whether additional mentoring improved skill learning, skill transfer and cognitive workloads in robotic surgery simulation training. Thirty-two surgical trainees were randomly assigned to either the Control-Group (CG) or Experiment-Group (EG). While the CG participants reviewed the PM at their discretion, the EG participants had explanations about PM and instructions on how to improve scores. Each subject completed a 5-week training using four simulation tasks. Pre- and post-training data were collected using both a simulator and robot. Peri-training data were collected after each session. Skill learning, time spent on PM (TPM), and cognitive workloads were compared between groups. After the simulation training, CG showed substantially lower simulation task scores (82.9 ± 6.0) compared with EG (93.2 ± 4.8). Both groups demonstrated improved physical model tasks performance with the actual robot, but the EG had a greater improvement in two tasks. The EG exhibited lower global mental workload/distress, higher engagement, and a better understanding regarding using PM to improve performance. The EG's TPM was initially long but substantially shortened as the group became familiar with PM. Our study demonstrated that the current VR simulator offered limited self-skill learning and additional mentoring still played an important role in improving the robotic surgery simulation training.
Intelligent manipulation technique for multi-branch robotic systems

Science.gov (United States)

Chen, Alexander Y. K.; Chen, Eugene Y. S.

1990-01-01

New analytical development in kinematics planning is reported. The INtelligent KInematics Planner (INKIP) consists of the kinematics spline theory and the adaptive logic annealing process. Also, a novel framework of robot learning mechanism is introduced. The FUzzy LOgic Self Organized Neural Networks (FULOSONN) integrates fuzzy logic in commands, control, searching, and reasoning, the embedded expert system for nominal robotics knowledge implementation, and the self organized neural networks for the dynamic knowledge evolutionary process. Progress on the mechanical construction of SRA Advanced Robotic System (SRAARS) and the real time robot vision system is also reported. A decision was made to incorporate the Local Area Network (LAN) technology in the overall communication system.
First steps in teaching computational thinking through mobile technology and robotics

Directory of Open Access Journals (Sweden)

Titipan Phetsrikran

2017-07-01

Full Text Available rogramming, or computational thinking, is becoming recognized as a skill that should be taught in primary and secondary schools. One technique for teaching programming is to use robotics, but usually this requires students to program via a PC. The purpose of this study is to investigate the potential for using an iPad application and robot that enables children to learn programming skills. This paper describes an application containing puzzles that involve creating a program to guide the physical robot from a start point to a goal. The application sends commands and controls the robots via Bluetooth and runs on the iPad with iOS. An initial experiment performed in a high school in Thailand explores how mobile technology and educational robotics can be applied to computational thinking in schools. The findings showed that the use of mobile technology opens up alternative styles of interaction in the classroom with potential for highly collaborative activities and greater focus on the learning domain.
Evolutionary Fuzzy Control and Navigation for Two Wheeled Robots Cooperatively Carrying an Object in Unknown Environments.

Science.gov (United States)

Juang, Chia-Feng; Lai, Min-Ge; Zeng, Wan-Ting

2015-09-01

This paper presents a method that allows two wheeled, mobile robots to navigate unknown environments while cooperatively carrying an object. In the navigation method, a leader robot and a follower robot cooperatively perform either obstacle boundary following (OBF) or target seeking (TS) to reach a destination. The two robots are controlled by fuzzy controllers (FC) whose rules are learned through an adaptive fusion of continuous ant colony optimization and particle swarm optimization (AF-CACPSO), which avoids the time-consuming task of manually designing the controllers. The AF-CACPSO-based evolutionary fuzzy control approach is first applied to the control of a single robot to perform OBF. The learning approach is then applied to achieve cooperative OBF with two robots, where an auxiliary FC designed with the AF-CACPSO is used to control the follower robot. For cooperative TS, a rule for coordination of the two robots is developed. To navigate cooperatively, a cooperative behavior supervisor is introduced to select between cooperative OBF and cooperative TS. The performance of the AF-CACPSO is verified through comparisons with various population-based optimization algorithms for the OBF learning problem. Simulations and experiments verify the effectiveness of the approach for cooperative navigation of two robots.
Grooming : 12 is het nieuwe 16 en vaker dan je denkt

NARCIS (Netherlands)

Broekman, C.C.M.T.; Vries, A. de

2014-01-01

Kinderen worden regelmatig seksueel benaderd. Tijdens een onderzoeksproject voor een van onze opdrachtgevers loggen we in op Habbo-hotel. Tot onze verbazing worden we al snel meerdere keren benaderd met seksueel getinte opmerkingen. Die laten weinig aan de verbeelding over. Daar schrik je van. En
Multiresolutional schemata for unsupervised learning of autonomous robots for 3D space operation

Science.gov (United States)

Lacaze, Alberto; Meystel, Michael; Meystel, Alex

1994-01-01

This paper describes a novel approach to the development of a learning control system for autonomous space robot (ASR) which presents the ASR as a 'baby' -- that is, a system with no a priori knowledge of the world in which it operates, but with behavior acquisition techniques that allows it to build this knowledge from the experiences of actions within a particular environment (we will call it an Astro-baby). The learning techniques are rooted in the recursive algorithm for inductive generation of nested schemata molded from processes of early cognitive development in humans. The algorithm extracts data from the environment and by means of correlation and abduction, it creates schemata that are used for control. This system is robust enough to deal with a constantly changing environment because such changes provoke the creation of new schemata by generalizing from experiences, while still maintaining minimal computational complexity, thanks to the system's multiresolutional nature.
Perancangan Lengan Robot 5 Derajat Kebebasan Dengan Pendekatan Kinematika

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- Firmansyah

2014-10-01

Full Text Available This study discusses the design of arm robot model with 5 degree of freedom that is designed to be a small-scale model of the articulated robot industry to simulate the movement of the robots industry. The objective of this research is to build a real arm robot based on kinematic aspects with the movement of waist, shoulder, elbow, wrist pitch, wrist roll and gripper, and to analyze the robot movement. The design includes building the real arm robot based on Arduino Uno board controller and the movement of the robot using servo motor DC. The robot can be controlled automatically from the computer with the RS-232 or USB port interface and it learns about the kinematic of the robot’s arm when an experiment on the forward kinematic is accomplished. The robot was running well, with the maximum distance that can be reached by the robot on the coordinate axis x = 425 mm, y = 425 mm and z = 480 mm.
Implementation of self-organizing neural networks for visuo-motor control of an industrial robot.

Science.gov (United States)

Walter, J A; Schulten, K I

1993-01-01

The implementation of two neural network algorithms for visuo-motor control of an industrial robot (Puma 562) is reported. The first algorithm uses a vector quantization technique, the ;neural-gas' network, together with an error correction scheme based on a Widrow-Hoff-type learning rule. The second algorithm employs an extended self-organizing feature map algorithm. Based on visual information provided by two cameras, the robot learns to position its end effector without an external teacher. Within only 3000 training steps, the robot-camera system is capable of reducing the positioning error of the robot's end effector to approximately 0.1% of the linear dimension of the work space. By employing adaptive feedback the robot succeeds in compensating not only slow calibration drifts, but also sudden changes in its geometry. Hardware aspects of the robot-camera system are discussed.
A Self-Synthesis Approach to Perceptual Learning for Multisensory Fusion in Robotics

Science.gov (United States)

Axenie, Cristian; Richter, Christoph; Conradt, Jörg

2016-01-01

Biological and technical systems operate in a rich multimodal environment. Due to the diversity of incoming sensory streams a system perceives and the variety of motor capabilities a system exhibits there is no single representation and no singular unambiguous interpretation of such a complex scene. In this work we propose a novel sensory processing architecture, inspired by the distributed macro-architecture of the mammalian cortex. The underlying computation is performed by a network of computational maps, each representing a different sensory quantity. All the different sensory streams enter the system through multiple parallel channels. The system autonomously associates and combines them into a coherent representation, given incoming observations. These processes are adaptive and involve learning. The proposed framework introduces mechanisms for self-creation and learning of the functional relations between the computational maps, encoding sensorimotor streams, directly from the data. Its intrinsic scalability, parallelisation, and automatic adaptation to unforeseen sensory perturbations make our approach a promising candidate for robust multisensory fusion in robotic systems. We demonstrate this by applying our model to a 3D motion estimation on a quadrotor. PMID:27775621
A Self-Synthesis Approach to Perceptual Learning for Multisensory Fusion in Robotics

Directory of Open Access Journals (Sweden)

Cristian Axenie

2016-10-01

Full Text Available Biological and technical systems operate in a rich multimodal environment. Due to the diversity of incoming sensory streams a system perceives and the variety of motor capabilities a system exhibits there is no single representation and no singular unambiguous interpretation of such a complex scene. In this work we propose a novel sensory processing architecture, inspired by the distributed macro-architecture of the mammalian cortex. The underlying computation is performed by a network of computational maps, each representing a different sensory quantity. All the different sensory streams enter the system through multiple parallel channels. The system autonomously associates and combines them into a coherent representation, given incoming observations. These processes are adaptive and involve learning. The proposed framework introduces mechanisms for self-creation and learning of the functional relations between the computational maps, encoding sensorimotor streams, directly from the data. Its intrinsic scalability, parallelisation, and automatic adaptation to unforeseen sensory perturbations make our approach a promising candidate for robust multisensory fusion in robotic systems. We demonstrate this by applying our model to a 3D motion estimation on a quadrotor.
Educational resources and tools for robotic learning Recursos y herramientas didácticas para el aprendizaje de la robótica

Directory of Open Access Journals (Sweden)

Pablo Gil Vazquez

2012-07-01

Full Text Available Normal.dotm 0 0 1 139 795 Universidad de Salamanca 6 1 976 12.0 0 false 18 pt 18 pt 0 0 false false false /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} This paper discusses different teaching experiences which aims are the learning robotics in the university. These experiences are reflected in the development of several robotics courses and subjects at the University of Alicante. The authors have created various educational platforms or they have used tools of free distribution and open source for the implementation of these courses. The main objetive of these courses is to teach the design and implementation of robotic solutions to solve various problems not only such as the control, programming and handling of robot but also the assembly, building and programming of educational mini-robots. On the one hand, new teaching tools are used such as simulators and virtual labs which make flexible the learning of robot arms. On the other hand, competitions are used to motivate students because this way, the students put into action the skills learned through building and programming low-cost mini-robots. Normal.dotm 0 0 1 157 900 Universidad de Salamanca 7 1 1105 12.0 0 false 18 pt 18 pt 0 0 false false false /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso
Training Engineering Disciplines and Skills through Robot Projects

DEFF Research Database (Denmark)

Friesel, Anna

The popularity of robots in educational activities increased the last 10-15 years. Engineering education all over the world includes courses and projects involving design, use and programming of robots in a variety of programs at technical colleges and universities. At the same time there is a gr......The popularity of robots in educational activities increased the last 10-15 years. Engineering education all over the world includes courses and projects involving design, use and programming of robots in a variety of programs at technical colleges and universities. At the same time...... there is a growing interest to work with robots. Robotic skills are also highly requested in industrial companies. At the Technical University of Denmark, DTU Diplom, we have several projects involving building and programing robots in our bachelor programs in Electronics, Computer Science, IT and Mechanical...... Engineering. This presentation deals with our experience in robotic activities in different programs in order to enhance understanding of mathematics, physics and different technical disciplines in the named programs. We also observed the increased motivation for learning theory when we combine traditional...
Intelligent control and cooperation for mobile robots

Science.gov (United States)

Stingu, Petru Emanuel

The topic discussed in this work addresses the current research being conducted at the Automation & Robotics Research Institute in the areas of UAV quadrotor control and heterogenous multi-vehicle cooperation. Autonomy can be successfully achieved by a robot under the following conditions: the robot has to be able to acquire knowledge about the environment and itself, and it also has to be able to reason under uncertainty. The control system must react quickly to immediate challenges, but also has to slowly adapt and improve based on accumulated knowledge. The major contribution of this work is the transfer of the ADP algorithms from the purely theoretical environment to the complex real-world robotic platforms that work in real-time and in uncontrolled environments. Many solutions are adopted from those present in nature because they have been proven to be close to optimal in very different settings. For the control of a single platform, reinforcement learning algorithms are used to design suboptimal controllers for a class of complex systems that can be conceptually split in local loops with simpler dynamics and relatively weak coupling to the rest of the system. Optimality is enforced by having a global critic but the curse of dimensionality is avoided by using local actors and intelligent pre-processing of the information used for learning the optimal controllers. The system model is used for constructing the structure of the control system, but on top of that the adaptive neural networks that form the actors use the knowledge acquired during normal operation to get closer to optimal control. In real-world experiments, efficient learning is a strong requirement for success. This is accomplished by using an approximation of the system model to focus the learning for equivalent configurations of the state space. Due to the availability of only local data for training, neural networks with local activation functions are implemented. For the control of a formation

What do we learn about development from baby robots?

Science.gov (United States)

Oudeyer, Pierre-Yves

2017-01-01

Understanding infant development is one of the great scientific challenges of contemporary science. In addressing this challenge, robots have proven useful as they allow experimenters to model the developing brain and body and understand the processes by which new patterns emerge in sensorimotor, cognitive, and social domains. Robotics also complements traditional experimental methods in psychology and neuroscience, where only a few variables can be studied at the same time. Moreover, work with robots has enabled researchers to systematically explore the role of the body in shaping the development of skill. All told, this work has shed new light on development as a complex dynamical system. WIREs Cogn Sci 2017, 8:e1395. doi: 10.1002/wcs.1395 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.
Comparing L2 Word Learning through a Tablet or Real Objects: What Benefits Learning Most?

NARCIS (Netherlands)

Vlaar, M.A.J.; Verhagen, J.; Oudgenoeg-Paz, O.; Leseman, P.P.M.

2017-01-01

In child-robot interactions focused on language learning, tablets are often used to structure the interaction between the robot and the child. However, it is not clear how tablets affect children’s learning gains. Real-life objects are thought to benefit children’s word learning, but it is not clear
Muecas: A Multi-Sensor Robotic Head for Affective Human Robot Interaction and Imitation

Directory of Open Access Journals (Sweden)

Felipe Cid

2014-04-01

Full Text Available This paper presents a multi-sensor humanoid robotic head for human robot interaction. The design of the robotic head, Muecas, is based on ongoing research on the mechanisms of perception and imitation of human expressions and emotions. These mechanisms allow direct interaction between the robot and its human companion through the different natural language modalities: speech, body language and facial expressions. The robotic head has 12 degrees of freedom, in a human-like configuration, including eyes, eyebrows, mouth and neck, and has been designed and built entirely by IADeX (Engineering, Automation and Design of Extremadura and RoboLab. A detailed description of its kinematics is provided along with the design of the most complex controllers. Muecas can be directly controlled by FACS (Facial Action Coding System, the de facto standard for facial expression recognition and synthesis. This feature facilitates its use by third party platforms and encourages the development of imitation and of goal-based systems. Imitation systems learn from the user, while goal-based ones use planning techniques to drive the user towards a final desired state. To show the flexibility and reliability of the robotic head, the paper presents a software architecture that is able to detect, recognize, classify and generate facial expressions in real time using FACS. This system has been implemented using the robotics framework, RoboComp, which provides hardware-independent access to the sensors in the head. Finally, the paper presents experimental results showing the real-time functioning of the whole system, including recognition and imitation of human facial expressions.
A Critical Analysis of the Use of Remote Presence Robots in Nursing Education

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Louise Racine

2016-01-01

Full Text Available The exponential proliferation of e-learning programs has considerably changed the landscape of contemporary nursing education. Nursing programs are delivered through classroom, blended, fully computerized or distributive models. The aim of this paper is to provide a critical theoretical analysis of potential pitfalls of the utilization of remote robots in nursing education. Against the backdrop of the nature of nursing knowledge, the usefulness of robots in nursing education is appraised. Robots enable students living in remote geographical areas to learn in their communities. The lack of evidence to support the efficiency of remote presence robots in nursing education, in general, and in clinical nursing education, in particular, raises some questions. Robots may run the risk of dehumanizing nursing education and impoverishing the acquisition of critical thinking skills. A critical examination of the advantages and disadvantages of remote robots should inform nurse administrators and educators before making decisions to rely on this cyber-based technology to support the delivery of nursing programs in remote areas.
Towards Behavior Control for Evolutionary Robot Based on RL with ENN

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Jingan Yang

2012-03-01

Full Text Available This paper proposes a behavior-switching control strategy of anevolutionary robotics based on Artificial NeuralNetwork (ANN and Genetic Algorithms (GA. This method is able not only to construct thereinforcement learning models for autonomous robots and evolutionary robot modules thatcontrol behaviors and reinforcement learning environments, and but also to perform thebehavior-switching control and obstacle avoidance of an evolutionary robotics (ER intime-varying environments with static and moving obstacles by combining ANN and GA.The experimental results on thebasic behaviors and behavior-switching control have demonstrated that ourmethod can perform the decision-making strategy and parameters set opimization ofFNN and GA by learning and can escape successfully from the trap of a localminima and avoid \\emph{"motion deadlock" status} of humanoid soccer robotics agents,and reduce the oscillation of the planned trajectory betweenthe multiple obstacles by crossover and mutation. Some results of the proposed algorithmhave been successfully applied to our simulation humanoid robotics soccer team CIT3Dwhich won \\emph{the 1st prize} of RoboCup Championship and ChinaOpen2010 (July 2010 and \\emph{the $2^{nd}$ place}of the official RoboCup World Championship on 5-11 July, 2011 in Istanbul, Turkey.As compared with the conventional behavior network and the adaptive behavior method,the genetic encoding complexity of our algorithm is simplified, and the networkperformance and the {\\em convergence rate $\\rho$} have been greatlyimproved.
Allothetic and idiothetic sensor fusion in rat-inspired robot localization

Science.gov (United States)

Weitzenfeld, Alfredo; Fellous, Jean-Marc; Barrera, Alejandra; Tejera, Gonzalo

2012-06-01

We describe a spatial cognition model based on the rat's brain neurophysiology as a basis for new robotic navigation architectures. The model integrates allothetic (external visual landmarks) and idiothetic (internal kinesthetic information) cues to train either rat or robot to learn a path enabling it to reach a goal from multiple starting positions. It stands in contrast to most robotic architectures based on SLAM, where a map of the environment is built to provide probabilistic localization information computed from robot odometry and landmark perception. Allothetic cues suffer in general from perceptual ambiguity when trying to distinguish between places with equivalent visual patterns, while idiothetic cues suffer from imprecise motions and limited memory recalls. We experiment with both types of cues in different maze configurations by training rats and robots to find the goal starting from a fixed location, and then testing them to reach the same target from new starting locations. We show that the robot, after having pre-explored a maze, can find a goal with improved efficiency, and is able to (1) learn the correct route to reach the goal, (2) recognize places already visited, and (3) exploit allothetic and idiothetic cues to improve on its performance. We finally contrast our biologically-inspired approach to more traditional robotic approaches and discuss current work in progress.
Instructional Design Using an In-House Built Teaching Assistant Robot to Enhance Elementary School English-as-a-Foreign-Language Learning

Science.gov (United States)

Wu, Wen-Chi Vivian; Wang, Rong-Jyue; Chen, Nian-Shing

2015-01-01

This paper presents a design for a cutting-edge English program in which elementary school learners of English as a foreign language in Taiwan had lively interactions with a teaching assistant robot. Three dimensions involved in the design included (1) a pleasant and interactive classroom environment as the learning context, (2) a teaching…
Application of a model of instrumental conditioning to mobile robot control

Science.gov (United States)

Saksida, Lisa M.; Touretzky, D. S.

1997-09-01

Instrumental conditioning is a psychological process whereby an animal learns to associate its actions with their consequences. This type of learning is exploited in animal training techniques such as 'shaping by successive approximations,' which enables trainers to gradually adjust the animal's behavior by giving strategically timed reinforcements. While this is similar in principle to reinforcement learning, the real phenomenon includes many subtle effects not considered in the machine learning literature. In addition, a good deal of domain information is utilized by an animal learning a new task; it does not start from scratch every time it learns a new behavior. For these reasons, it is not surprising that mobile robot learning algorithms have yet to approach the sophistication and robustness of animal learning. A serious attempt to model instrumental learning could prove fruitful for improving machine learning techniques. In the present paper, we develop a computational theory of shaping at a level appropriate for controlling mobile robots. The theory is based on a series of mechanisms for 'behavior editing,' in which pre-existing behaviors, either innate or previously learned, can be dramatically changed in magnitude, shifted in direction, or otherwise manipulated so as to produce new behavioral routines. We have implemented our theory on Amelia, an RWI B21 mobile robot equipped with a gripper and color video camera. We provide results from training Amelia on several tasks, all of which were constructed as variations of one innate behavior, object-pursuit.
Robotics & artificial intelligence : The future of surgeons & surgery

Directory of Open Access Journals (Sweden)

K I Mathai

2016-01-01

Robots have evolved as dextrous, fatigue and tremor free surgical tools. The data crunching capability of computers is improving in speed and in capability for machine learning. Human surgical maturity on the other hand is attained and matures through phases of information assimilation, knowledge consolidation and attainment of surgical wisdom. Human surgeons at the helm will, in this decade harness robotic capabilities and information template paradigms to fine tune many procedures and to augment surgical reach. Quantum leaps and paradigm shifts towards robotic surgical autonomy may be neither desirable nor practical.
ALLIANCE: An architecture for fault tolerant multi-robot cooperation

Energy Technology Data Exchange (ETDEWEB)

Parker, L.E.

1995-02-01

ALLIANCE is a software architecture that facilitates the fault tolerant cooperative control of teams of heterogeneous mobile robots performing missions composed of loosely coupled, largely independent subtasks. ALLIANCE allows teams of robots, each of which possesses a variety of high-level functions that it can perform during a mission, to individually select appropriate actions throughout the mission based on the requirements of the mission, the activities of other robots, the current environmental conditions, and the robot`s own internal states. ALLIANCE is a fully distributed, behavior-based architecture that incorporates the use of mathematically modeled motivations (such as impatience and acquiescence) within each robot to achieve adaptive action selection. Since cooperative robotic teams usually work in dynamic and unpredictable environments, this software architecture allows the robot team members to respond robustly, reliably, flexibly, and coherently to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. The feasibility of this architecture is demonstrated in an implementation on a team of mobile robots performing a laboratory version of hazardous waste cleanup.
Robotic Toys as a Catalyst for Mathematical Problem Solving

Science.gov (United States)

Highfield, Kate

2010-01-01

Robotic toys present unique opportunities for teachers of young children to integrate mathematics learning with engaging problem-solving tasks. This article describes a series of tasks using Bee-bots and Pro-bots, developed as part a larger project examining young children's use of robotic toys as tools in developing mathematical and metacognitive…
Perceptions, practices and health seeking behaviour constrain JE/AES interventions in high endemic district of North India.

Science.gov (United States)

Chaturvedi, Sanjay; Sharma, Neha; Kakkar, Manish

2017-08-08

Acute Encephalitis Syndrome (AES) and Japanese Encephalitis (JE) stay as poorly understood phenomena in India. Multiple linkages to determinants such as poverty, socio-economic status, gender, environment, and population distribution, make it a greater developmental issue than just a zoonotic disease. A qualitative study was conducted to map knowledge, perceptions and practices of community and health systems level stakeholders. Seventeen interviews with utilizers of AES care, care givers from human and veterinary sectors, Non-governmental Organizations (NGOs), and pig owners and 4 Focused Group Discussions (FGDs) with farmers, community leaders, and students were conducted in an endemic north Indian district-Kushinagar. Core themes that emerged were: JE/AES been perceived as a deadly disease, but not a major health problem; filthy conditions, filthy water and mosquitoes seen to be associated with JE/AES; pigs not seen as a source of infection; minimal role of government health workers in the first-contact care of acute Illness; no social or cultural resistance to JE vaccination or mosquito control; no gender-based discrimination in the care of acute Illness; and non-utilization of funds available with local self govt. Serious challenges and systematic failures in delivery of care during acute illness, which can critically inform the health systems, were also identified. There is an urgent need for promotive interventions to address lack of awareness about the drivers of JE/AES. Delivery of care during acute illness suffers with formidable challenges and systematic failures. A large portion of mortality can be prevented by early institution of rational management at primary and secondary level, and by avoiding wastage of time and resources for investigations and medications that are not actually required.
Decision Making in Reinforcement Learning Using a Modified Learning Space Based on the Importance of Sensors

Directory of Open Access Journals (Sweden)

Yasutaka Kishima

2013-01-01

Full Text Available Many studies have been conducted on the application of reinforcement learning (RL to robots. A robot which is made for general purpose has redundant sensors or actuators because it is difficult to assume an environment that the robot will face and a task that the robot must execute. In this case, -space on RL contains redundancy so that the robot must take much time to learn a given task. In this study, we focus on the importance of sensors with regard to a robot’s performance of a particular task. The sensors that are applicable to a task differ according to the task. By using the importance of the sensors, we try to adjust the state number of the sensors and to reduce the size of -space. In this paper, we define the measure of importance of a sensor for a task with the correlation between the value of each sensor and reward. A robot calculates the importance of the sensors and makes the size of -space smaller. We propose the method which reduces learning space and construct the learning system by putting it in RL. In this paper, we confirm the effectiveness of our proposed system with an experimental robot.
Promoting Diversity in Undergraduate Research in Robotics-Based Seismic

Science.gov (United States)

Gifford, C. M.; Arthur, C. L.; Carmichael, B. L.; Webber, G. K.; Agah, A.

2006-12-01

The motivation for this research was to investigate forming evenly-spaced grid patterns with a team of mobile robots for future use in seismic imaging in polar environments. A team of robots was incrementally designed and simulated by incorporating sensors and altering each robot's controller. Challenges, design issues, and efficiency were also addressed. This research project incorporated the efforts of two undergraduate REU students from Elizabeth City State University (ECSU) in North Carolina, and the research staff at the Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas. ECSU is a historically black university. Mentoring these two minority students in scientific research, seismic, robotics, and simulation will hopefully encourage them to pursue graduate degrees in science-related or engineering fields. The goals for this 10-week internship during summer 2006 were to educate the students in the fields of seismology, robotics, and virtual prototyping and simulation. Incrementally designing a robot platform for future enhancement and evaluation was central to this research, and involved simulation of several robots working together to change seismic grid shape and spacing. This process gave these undergraduate students experience and knowledge in an actual research project for a real-world application. The two undergraduate students gained valuable research experience and advanced their knowledge of seismic imaging, robotics, sensors, and simulation. They learned that seismic sensors can be used in an array to gather 2D and 3D images of the subsurface. They also learned that robotics can support dangerous or difficult human activities, such as those in a harsh polar environment, by increasing automation, robustness, and precision. Simulating robot designs also gave them experience in programming behaviors for mobile robots. Thus far, one academic paper has resulted from their research. This paper received third place at the 2006
Robotic bariatric surgery: a systematic review.

Science.gov (United States)

Fourman, Matthew M; Saber, Alan A

2012-01-01

Obesity is a nationwide epidemic, and the only evidence-based, durable treatment of this disease is bariatric surgery. This field has evolved drastically during the past decade. One of the latest advances has been the increased use of robotics within this field. The goal of our study was to perform a systematic review of the recent data to determine the safety and efficacy of robotic bariatric surgery. The setting was the University Hospitals Case Medical Center (Cleveland, OH). A PubMed search was performed for robotic bariatric surgery from 2005 to 2011. The inclusion criteria were English language, original research, human, and bariatric surgical procedures. Perioperative data were then collected from each study and recorded. A total of 18 studies were included in our review. The results of our systematic review showed that bariatric surgery, when performed with the use of robotics, had similar or lower complication rates compared with traditional laparoscopy. Two studies showed shorter operative times using the robot for Roux-en-Y gastric bypass, but 4 studies showed longer operative times in the robotic arm. In addition, the learning curve appears to be shorter when robotic gastric bypass is compared with the traditional laparoscopic approach. Most investigators agreed that robotic laparoscopic surgery provides superior imaging and freedom of movement compared with traditional laparoscopy. The application of robotics appears to be a safe option within the realm of bariatric surgery. Prospective randomized trials comparing robotic and laparoscopic outcomes are needed to further define the role of robotics within the field of bariatric surgery. Longer follow-up times would also help elucidate any long-term outcomes differences with the use of robotics versus traditional laparoscopy. Copyright © 2012 American Society for Metabolic and Bariatric Surgery. All rights reserved.
Robotics and artificial intelligence for hazardous environments

International Nuclear Information System (INIS)

Spelt, P.F.

1993-01-01

In our technological society, hazardous materials including toxic chemicals, flammable, explosive, and radioactive substances, and biological agents, are used and handled routinely. Each year, many workers who handle these substances are accidently contaminated, in some cases resulting in injury, death, or chronic disabilities. If these hazardous materials could be handled remotely, either with a teleoperated robot (operated by a worker in a safe location) or by an autonomous robot, then human suffering and economic costs of accidental exposures could be dramatically reduced. At present, it is still difficult for commercial robotic technology to completely replace humans involved in performing complex work tasks in hazardous environments. The robotics efforts at the Center for Engineering Systems Advanced Research represent a significant effort at contributing to the advancement of robotics for use in hazardous environments. While this effort is very broad-based, ranging from dextrous manipulation to mobility and integrated sensing, the technical portion of this paper will focus on machine learning and the high-level decision making needed for autonomous robotics
Robot-assisted surgery for gastric cancer

Science.gov (United States)

Procopiuc, Livia; Tudor, Ştefan; Mănuc, Mircea; Diculescu, Mircea; Vasilescu, Cătălin

2016-01-01

Minimally invasive surgery for gastric cancer is a relatively new research field, with convincing results mostly stemming from Asian countries. The use of the robotic surgery platform, thus far assessed as a safe procedure, which is also easier to learn, sets the background for a wider spread of minimally invasive technique in the treatment of gastric cancer. This review will cover the literature published so far, analyzing the pros and cons of robotic surgery and highlighting the remaining study questions. PMID:26798433
Robot-Aided Neurorehabilitation: A Pediatric Robot for Ankle Rehabilitation.

Science.gov (United States)

Michmizos, Konstantinos P; Rossi, Stefano; Castelli, Enrico; Cappa, Paolo; Krebs, Hermano Igo

2015-11-01

This paper presents the pediAnklebot, an impedance-controlled low-friction, backdriveable robotic device developed at the Massachusetts Institute of Technology that trains the ankle of neurologically impaired children of ages 6-10 years old. The design attempts to overcome the known limitations of the lower extremity robotics and the unknown difficulties of what constitutes an appropriate therapeutic interaction with children. The robot's pilot clinical evaluation is on-going and it incorporates our recent findings on the ankle sensorimotor control in neurologically intact subjects, namely the speed-accuracy tradeoff, the deviation from an ideally smooth ankle trajectory, and the reaction time. We used these concepts to develop the kinematic and kinetic performance metrics that guided the ankle therapy in a similar fashion that we have done for our upper extremity devices. Here we report on the use of the device in at least nine training sessions for three neurologically impaired children. Results demonstrated a statistically significant improvement in the performance metrics assessing explicit and implicit motor learning. Based on these initial results, we are confident that the device will become an effective tool that harnesses plasticity to guide habilitation during childhood.
Reverse control for humanoid robot task recognition.

Science.gov (United States)

Hak, Sovannara; Mansard, Nicolas; Stasse, Olivier; Laumond, Jean Paul

2012-12-01

Efficient methods to perform motion recognition have been developed using statistical tools. Those methods rely on primitive learning in a suitable space, for example, the latent space of the joint angle and/or adequate task spaces. Learned primitives are often sequential: A motion is segmented according to the time axis. When working with a humanoid robot, a motion can be decomposed into parallel subtasks. For example, in a waiter scenario, the robot has to keep some plates horizontal with one of its arms while placing a plate on the table with its free hand. Recognition can thus not be limited to one task per consecutive segment of time. The method presented in this paper takes advantage of the knowledge of what tasks the robot is able to do and how the motion is generated from this set of known controllers, to perform a reverse engineering of an observed motion. This analysis is intended to recognize parallel tasks that have been used to generate a motion. The method relies on the task-function formalism and the projection operation into the null space of a task to decouple the controllers. The approach is successfully applied on a real robot to disambiguate motion in different scenarios where two motions look similar but have different purposes.
Embodied Evolution in Collective Robotics: A Review

Directory of Open Access Journals (Sweden)

Nicolas Bredeche

2018-02-01

Full Text Available This article provides an overview of evolutionary robotics techniques applied to online distributed evolution for robot collectives, namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. This article also presents a comprehensive summary of research published in the field since its inception around the year 2000, providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots to embodied evolution as an online distributed learning method for designing collective behaviors in swarm-like collectives. This article concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.

Laws on Robots, Laws by Robots, Laws in Robots : Regulating Robot Behaviour by Design

NARCIS (Netherlands)

Leenes, R.E.; Lucivero, F.

2015-01-01

Speculation about robot morality is almost as old as the concept of a robot itself. Asimov’s three laws of robotics provide an early and well-discussed example of moral rules robots should observe. Despite the widespread influence of the three laws of robotics and their role in shaping visions of
A System on Chip approach to enhanced learning in interdisciplinary robotics

DEFF Research Database (Denmark)

Sørensen, Anders Stengaard; Falsig, Simon

2011-01-01

the framework in an embedded systems course and various student projects, and have found that it greatly enhance the students abilities to control hardware from software, and dramatically reduce the time spent on software $\\leftrightarrow$ hardware interfacing. As the framework is also scalable, it can support......p, li { white-space: pre-wrap; } To sustain interdisciplinary teaching and learning in the rapidly growing and diversifying field of robotics, we have successfully employed FPGA based System on Chip (SoC) technology to provide abstraction between high level software and low level IO/ and control...... hardware. Our approach is to provides students with a simple FPGA based framework for hardware access, and hardware I/O development, which is independent of computer platform and programming language, and enable the students to add to, or change I/O hardware in accordance with their skills. We have tested...
Spatial Ability Learning through Educational Robotics

Science.gov (United States)

Julià, Carme; Antolí, Juan Òscar

2016-01-01

Several authors insist on the importance of students' acquisition of spatial abilities and visualization in order to have academic success in areas such as science, technology or engineering. This paper proposes to discuss and analyse the use of educational robotics to develop spatial abilities in 12 year old students. First of all, a course to…
iPathology: Robotic Applications and Management of Plants and Plant Diseases

Directory of Open Access Journals (Sweden)

Yiannis Ampatzidis

2017-06-01

Full Text Available The rapid development of new technologies and the changing landscape of the online world (e.g., Internet of Things (IoT, Internet of All, cloud-based solutions provide a unique opportunity for developing automated and robotic systems for urban farming, agriculture, and forestry. Technological advances in machine vision, global positioning systems, laser technologies, actuators, and mechatronics have enabled the development and implementation of robotic systems and intelligent technologies for precision agriculture. Herein, we present and review robotic applications on plant pathology and management, and emerging agricultural technologies for intra-urban agriculture. Greenhouse advanced management systems and technologies have been greatly developed in the last years, integrating IoT and WSN (Wireless Sensor Network. Machine learning, machine vision, and AI (Artificial Intelligence have been utilized and applied in agriculture for automated and robotic farming. Intelligence technologies, using machine vision/learning, have been developed not only for planting, irrigation, weeding (to some extent, pruning, and harvesting, but also for plant disease detection and identification. However, plant disease detection still represents an intriguing challenge, for both abiotic and biotic stress. Many recognition methods and technologies for identifying plant disease symptoms have been successfully developed; still, the majority of them require a controlled environment for data acquisition to avoid false positives. Machine learning methods (e.g., deep and transfer learning present promising results for improving image processing and plant symptom identification. Nevertheless, diagnostic specificity is a challenge for microorganism control and should drive the development of mechatronics and robotic solutions for disease management.
MARS: An Educational Environment for Multiagent Robot Simulations

Directory of Open Access Journals (Sweden)

Marco Casini

2016-01-01

Full Text Available Undergraduate robotics students often find it difficult to design and validate control algorithms for teams of mobile robots. This is mainly due to two reasons. First, very rarely, educational laboratories are equipped with large teams of robots, which are usually expensive, bulky, and difficult to manage and maintain. Second, robotics simulators often require students to spend much time to learn their use and functionalities. For this purpose, a simulator of multiagent mobile robots named MARS has been developed within the Matlab environment, with the aim of helping students to simulate a wide variety of control algorithms in an easy way and without spending time for understanding a new language. Through this facility, the user is able to simulate multirobot teams performing different tasks, from cooperative to competitive ones, by using both centralized and distributed controllers. Virtual sensors are provided to simulate real devices. A graphical user interface allows students to monitor the robots behaviour through an online animation.
Using Functional Electrical Stimulation Mediated by Iterative Learning Control and Robotics to Improve Arm Movement for People With Multiple Sclerosis.

Science.gov (United States)

Sampson, Patrica; Freeman, Chris; Coote, Susan; Demain, Sara; Feys, Peter; Meadmore, Katie; Hughes, Ann-Marie

2016-02-01

Few interventions address multiple sclerosis (MS) arm dysfunction but robotics and functional electrical stimulation (FES) appear promising. This paper investigates the feasibility of combining FES with passive robotic support during virtual reality (VR) training tasks to improve upper limb function in people with multiple sclerosis (pwMS). The system assists patients in following a specified trajectory path, employing an advanced model-based paradigm termed iterative learning control (ILC) to adjust the FES to improve accuracy and maximise voluntary effort. Reaching tasks were repeated six times with ILC learning the optimum control action from previous attempts. A convenience sample of five pwMS was recruited from local MS societies, and the intervention comprised 18 one-hour training sessions over 10 weeks. The accuracy of tracking performance without FES and the amount of FES delivered during training were analyzed using regression analysis. Clinical functioning of the arm was documented before and after treatment with standard tests. Statistically significant results following training included: improved accuracy of tracking performance both when assisted and unassisted by FES; reduction in maximum amount of FES needed to assist tracking; and less impairment in the proximal arm that was trained. The system was well tolerated by all participants with no increase in muscle fatigue reported. This study confirms the feasibility of FES combined with passive robot assistance as a potentially effective intervention to improve arm movement and control in pwMS and provides the basis for a follow-up study.
Vision-based mobile robot navigation through deep convolutional neural networks and end-to-end learning

Science.gov (United States)

Zhang, Yachu; Zhao, Yuejin; Liu, Ming; Dong, Liquan; Kong, Lingqin; Liu, Lingling

2017-09-01

In contrast to humans, who use only visual information for navigation, many mobile robots use laser scanners and ultrasonic sensors along with vision cameras to navigate. This work proposes a vision-based robot control algorithm based on deep convolutional neural networks. We create a large 15-layer convolutional neural network learning system and achieve the advanced recognition performance. Our system is trained from end to end to map raw input images to direction in supervised mode. The images of data sets are collected in a wide variety of weather conditions and lighting conditions. Besides, the data sets are augmented by adding Gaussian noise and Salt-and-pepper noise to avoid overfitting. The algorithm is verified by two experiments, which are line tracking and obstacle avoidance. The line tracking experiment is proceeded in order to track the desired path which is composed of straight and curved lines. The goal of obstacle avoidance experiment is to avoid the obstacles indoor. Finally, we get 3.29% error rate on the training set and 5.1% error rate on the test set in the line tracking experiment, 1.8% error rate on the training set and less than 5% error rate on the test set in the obstacle avoidance experiment. During the actual test, the robot can follow the runway centerline outdoor and avoid the obstacle in the room accurately. The result confirms the effectiveness of the algorithm and our improvement in the network structure and train parameters
Functional electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke

Directory of Open Access Journals (Sweden)

Meadmore Katie L

2012-06-01

Full Text Available Abstract Background Novel stroke rehabilitation techniques that employ electrical stimulation (ES and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients’ voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL, a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort. Methods Five hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants’ arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression. Results From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced. Conclusions The concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this.
ALLIANCE: An architecture for fault tolerant multi-robot cooperation

International Nuclear Information System (INIS)

Parker, L.E.

1995-02-01

ALLIANCE is a software architecture that facilitates the fault tolerant cooperative control of teams of heterogeneous mobile robots performing missions composed of loosely coupled, largely independent subtasks. ALLIANCE allows teams of robots, each of which possesses a variety of high-level functions that it can perform during a mission, to individually select appropriate actions throughout the mission based on the requirements of the mission, the activities of other robots, the current environmental conditions, and the robot's own internal states. ALLIANCE is a fully distributed, behavior-based architecture that incorporates the use of mathematically modeled motivations (such as impatience and acquiescence) within each robot to achieve adaptive action selection. Since cooperative robotic teams usually work in dynamic and unpredictable environments, this software architecture allows the robot team members to respond robustly, reliably, flexibly, and coherently to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. The feasibility of this architecture is demonstrated in an implementation on a team of mobile robots performing a laboratory version of hazardous waste cleanup
Children and Robots Learning to Play Hide and Seek

National Research Council Canada - National Science Library

Trafton, J. G; Schultz, Alan C; Perznowski, Dennis; Bugajska, Magdalena D; Adams, William; Cassimatis, Nicholas L; Brock, Derek P

2006-01-01

...., containment, under) and use that information to play a credible game of hide and seek. They model this hypothesis within the ACT-R cognitive architecture and put the model on a robot, which is able to mimic the child's hiding behavior. They also take the "hiding" model and use it as the basis for a "seeking" model. They suggest that using the same representations and procedures that a person uses allows better interaction between the human and robotic system.
Vision Guided Intelligent Robot Design And Experiments

Science.gov (United States)

Slutzky, G. D.; Hall, E. L.

1988-02-01

The concept of an intelligent robot is an important topic combining sensors, manipulators, and artificial intelligence to design a useful machine. Vision systems, tactile sensors, proximity switches and other sensors provide the elements necessary for simple game playing as well as industrial applications. These sensors permit adaption to a changing environment. The AI techniques permit advanced forms of decision making, adaptive responses, and learning while the manipulator provides the ability to perform various tasks. Computer languages such as LISP and OPS5, have been utilized to achieve expert systems approaches in solving real world problems. The purpose of this paper is to describe several examples of visually guided intelligent robots including both stationary and mobile robots. Demonstrations will be presented of a system for constructing and solving a popular peg game, a robot lawn mower, and a box stacking robot. The experience gained from these and other systems provide insight into what may be realistically expected from the next generation of intelligent machines.
Robotics in surgery: is a robot necessary? For what?

Science.gov (United States)

Ross, Sharona B; Downs, Darrell; Saeed, Sabrina M; Dolce, John K; Rosemurgy, Alexander S

2017-02-01

Every operation can be categorized along a spectrum from "most invasive" to "least invasive", based on the approach(es) through which it is commonly undertaken. Operations that are considered "most invasive" are characterized by "open" approaches with a relatively high degree of morbidity, while operations that are considered "least invasive" are undertaken with minimally invasive techniques and are associated with relatively improved patient outcomes, including faster recovery times and fewer complications. Because of the potential for reduced morbidity, movement along the spectrum towards minimally invasive surgery (MIS) is associated with a host of salutary benefits and, as well, lower costs of patient care. Accordingly, the goal of all stakeholders in surgery should be to attain universal application of the most minimally invasive approaches. Yet the difficulty of performing minimally invasive operations has largely limited its widespread application in surgery, particularly in the context of complex operations (i.e., those requiring complex extirpation and/or reconstruction). Robotic surgery, however, may facilitate application of minimally invasive techniques requisite for particular operations. Enhancements in visualization and dexterity offered by robotic surgical systems allow busy surgeons to quickly gain proficiency in demanding techniques (e.g., pancreaticojejunostomy), within a short learning curve. That is not to say, however, that all operations undertaken with minimally invasive techniques require robotic technology. Herein, we attempt to define how surgeon skill, operative difficulty, patient outcomes, and cost factors determine when robotic technology should be reasonably applied to patient care in surgery.
Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control.

Science.gov (United States)

Reinhart, René Felix; Shareef, Zeeshan; Steil, Jochen Jakob

2017-02-08

Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant's intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms.
Getting started with robotics in general surgery with cholecystectomy: the Canadian experience

Science.gov (United States)

Jayaraman, Shiva; Davies, Ward; Schlachta, Christopher M.

2009-01-01

Background The value of robotics in general surgery may be for advanced minimally invasive procedures. Unlike other specialties, formal fellowship training opportunities for robotic general surgery are few. As a result, most surgeons currently develop robotic skills in practice. Our goal was to determine whether robotic cholecystectomy is a safe and effective bridge to advanced robotics in general surgery. Methods Before performing advanced robotic procedures, 2 surgeons completed the Intuitive Surgical da Vinci training course and agreed to work together on all procedures. Clinical surgery began with da Vinci cholecystectomy with a plan to begin advanced procedures after at least 10 cholecystectomies. We performed a retrospective review of our pilot series of robotic cholecystectomies and compared them with contemporaneous laparoscopic controls. The primary outcome was safety, and the secondary outcome was learning curve. Results There were 16 procedures in the robotics arm and 20 in the laparoscopic arm. Two complications (da Vinci port-site hernia, transient elevation of liver enzymes) occurred in the robotic arm, whereas only 1 laparoscopic patient (slow to awaken from anesthetic) experienced a complication. None was significant. The mean time required to perform robotic cholecystectomy was significantly longer than laparoscopic surgery (91 v. 41 min, p robotic procedures (14 v. 11 min, p = 0.015). We observed a trend showing longer mean anesthesia time for robotic procedures (23 v. 15 min). Regarding learning curve, the mean operative time needed for the first 3 robotic procedures was longer than for the last 3 (101 v. 80 min); however, this difference was not significant. Since this experience, the team has confidently gone on to perform robotic biliary, pancreatic, gastresophageal, intestinal and colorectal operations. Conclusion Robotic cholecystectomy can be performed reliably; however, owing to the significant increase in operating room resources, it
Getting started with robotics in general surgery with cholecystectomy: the Canadian experience.

Science.gov (United States)

Jayaraman, Shiva; Davies, Ward; Schlachta, Christopher M

2009-10-01

The value of robotics in general surgery may be for advanced minimally invasive procedures. Unlike other specialties, formal fellowship training opportunities for robotic general surgery are few. As a result, most surgeons currently develop robotic skills in practice. Our goal was to determine whether robotic cholecystectomy is a safe and effective bridge to advanced robotics in general surgery. Before performing advanced robotic procedures, 2 surgeons completed the Intuitive Surgical da Vinci training course and agreed to work together on all procedures. Clinical surgery began with da Vinci cholecystectomy with a plan to begin advanced procedures after at least 10 cholecystectomies. We performed a retrospective review of our pilot series of robotic cholecystectomies and compared them with contemporaneous laparoscopic controls. The primary outcome was safety, and the secondary outcome was learning curve. There were 16 procedures in the robotics arm and 20 in the laparoscopic arm. Two complications (da Vinci port-site hernia, transient elevation of liver enzymes) occurred in the robotic arm, whereas only 1 laparoscopic patient (slow to awaken from anesthetic) experienced a complication. None was significant. The mean time required to perform robotic cholecystectomy was significantly longer than laparoscopic surgery (91 v. 41 min, p robotic procedures (14 v. 11 min, p = 0.015). We observed a trend showing longer mean anesthesia time for robotic procedures (23 v. 15 min). Regarding learning curve, the mean operative time needed for the first 3 robotic procedures was longer than for the last 3 (101 v. 80 min); however, this difference was not significant. Since this experience, the team has confidently gone on to perform robotic biliary, pancreatic, gastresophageal, intestinal and colorectal operations. Robotic cholecystectomy can be performed reliably; however, owing to the significant increase in operating room resources, it cannot be justified for routine use. Our
Origami-based earthworm-like locomotion robots.

Science.gov (United States)

Fang, Hongbin; Zhang, Yetong; Wang, K W

2017-10-16

Inspired by the morphology characteristics of the earthworms and the excellent deformability of origami structures, this research creates a novel earthworm-like locomotion robot through exploiting the origami techniques. In this innovation, appropriate actuation mechanisms are incorporated with origami ball structures into the earthworm-like robot 'body', and the earthworm's locomotion mechanism is mimicked to develop a gait generator as the robot 'centralized controller'. The origami ball, which is a periodic repetition of waterbomb units, could output significant bidirectional (axial and radial) deformations in an antagonistic way similar to the earthworm's body segment. Such bidirectional deformability can be strategically programmed by designing the number of constituent units. Experiments also indicate that the origami ball possesses two outstanding mechanical properties that are beneficial to robot development: one is the structural multistability in the axil direction that could contribute to the robot control implementation; and the other is the structural compliance in the radial direction that would increase the robot robustness and applicability. To validate the origami-based innovation, this research designs and constructs three robot segments based on different axial actuators: DC-motor, shape-memory-alloy springs, and pneumatic balloon. Performance evaluations reveal their merits and limitations, and to prove the concept, the DC-motor actuation is selected for building a six-segment robot prototype. Learning from earthworms' fundamental locomotion mechanism-retrograde peristalsis wave, seven gaits are automatically generated; controlled by which, the robot could achieve effective locomotion with qualitatively different modes and a wide range of average speeds. The outcomes of this research could lead to the development of origami locomotion robots with low fabrication costs, high customizability, light weight, good scalability, and excellent re-configurability.
Perceptions, practices and health seeking behaviour constrain JE/AES interventions in high endemic district of North India

Directory of Open Access Journals (Sweden)

Sanjay Chaturvedi

2017-08-01

Full Text Available Abstract Background Acute Encephalitis Syndrome (AES and Japanese Encephalitis (JE stay as poorly understood phenomena in India. Multiple linkages to determinants such as poverty, socio-economic status, gender, environment, and population distribution, make it a greater developmental issue than just a zoonotic disease. Methods A qualitative study was conducted to map knowledge, perceptions and practices of community and health systems level stakeholders. Seventeen interviews with utilizers of AES care, care givers from human and veterinary sectors, Non-governmental Organizations (NGOs, and pig owners and 4 Focused Group Discussions (FGDs with farmers, community leaders, and students were conducted in an endemic north Indian district-Kushinagar. Results Core themes that emerged were: JE/AES been perceived as a deadly disease, but not a major health problem; filthy conditions, filthy water and mosquitoes seen to be associated with JE/AES; pigs not seen as a source of infection; minimal role of government health workers in the first-contact care of acute Illness; no social or cultural resistance to JE vaccination or mosquito control; no gender-based discrimination in the care of acute Illness; and non-utilization of funds available with local self govt. Serious challenges and systematic failures in delivery of care during acute illness, which can critically inform the health systems, were also identified. Conclusion There is an urgent need for promotive interventions to address lack of awareness about the drivers of JE/AES. Delivery of care during acute illness suffers with formidable challenges and systematic failures. A large portion of mortality can be prevented by early institution of rational management at primary and secondary level, and by avoiding wastage of time and resources for investigations and medications that are not actually required.
Bijblijven op je vakgebied nieuwe stijl! : Awareness tools voor e-journals

NARCIS (Netherlands)

Bijker, Alie; van den Brekel, Guus

2013-01-01

"Ik probeer regelmatig een aantal favoriete tijdschriften via email alerts te lezen, maar blijf het omslachtig vinden en mijn mailbox raakt maar voller en voller!" "Ik vind het lastig om op mijn tablet of via de email vanaf thuis toegang te krijgen tot de full text van artikelen" Hoe blijf je op de
Robotic surgery start-up with a fellow as the console surgeon

DEFF Research Database (Denmark)

Reinhardt, Susanne; Ifaoui, Inge Boetker; Thorup, Jorgen

2017-01-01

learning curve for robotic pyeloplasty will allow pediatric urology fellowship programs to be integrated in the start-up phase of a pediatric robotic program even though the case material is limited. Operative success rates were in accordance with the gold standard of open surgery....
Friendly network robotics; Friendly network robotics

Energy Technology Data Exchange (ETDEWEB)

NONE

1997-03-01

This paper summarizes the research results on the friendly network robotics in fiscal 1996. This research assumes an android robot as an ultimate robot and the future robot system utilizing computer network technology. The robot aiming at human daily work activities in factories or under extreme environments is required to work under usual human work environments. The human robot with similar size, shape and functions to human being is desirable. Such robot having a head with two eyes, two ears and mouth can hold a conversation with human being, can walk with two legs by autonomous adaptive control, and has a behavior intelligence. Remote operation of such robot is also possible through high-speed computer network. As a key technology to use this robot under coexistence with human being, establishment of human coexistent robotics was studied. As network based robotics, use of robots connected with computer networks was also studied. In addition, the R-cube (R{sup 3}) plan (realtime remote control robot technology) was proposed. 82 refs., 86 figs., 12 tabs.

Vision Based Autonomous Robotic Control for Advanced Inspection and Repair

Science.gov (United States)

Wehner, Walter S.

2014-01-01

The advanced inspection system is an autonomous control and analysis system that improves the inspection and remediation operations for ground and surface systems. It uses optical imaging technology with intelligent computer vision algorithms to analyze physical features of the real-world environment to make decisions and learn from experience. The advanced inspection system plans to control a robotic manipulator arm, an unmanned ground vehicle and cameras remotely, automatically and autonomously. There are many computer vision, image processing and machine learning techniques available as open source for using vision as a sensory feedback in decision-making and autonomous robotic movement. My responsibilities for the advanced inspection system are to create a software architecture that integrates and provides a framework for all the different subsystem components; identify open-source algorithms and techniques; and integrate robot hardware.
THE DEVELOPMENT OF AN ARTIFICIAL INTELIGENT FOR SMART MOBILE ROBOT USING SCHEMA EXTRACTION MECHANISM

Directory of Open Access Journals (Sweden)

Mokh. Sholihul Hadi

2012-09-01

Full Text Available Abstract: Recently, a number of skillful robots have been developed. One of them can walk and move upstairs just like human beings. However it can so far only demonstrate preprogrammed motions according to the external commands. Therefore an autonomous adaptation ability has been highly anticipated. Meanwhile, humans can learn new motions such as catching or kicking a ball, in spite of their high dimensional sensorimotor degree of freedom. In this learning process, it can be hypothesized that the learner actively constrains the DOF by their self using learning skills, in this paper referred to as schema. In this study, a learning method for smart mobile robots operating in unknown environments is proposed, where not only a learning mechanism for sensorimotor mappings but also an extraction or reuse mechanism of the schemata is implemented. Through the results of simulations and real experiments of smart mobile robot navigation, the validity of the proposed method is clarified.
Robot vision for nuclear advanced robot

International Nuclear Information System (INIS)

Nakayama, Ryoichi; Okano, Hideharu; Kuno, Yoshinori; Miyazawa, Tatsuo; Shimada, Hideo; Okada, Satoshi; Kawamura, Astuo

1991-01-01

This paper describes Robot Vision and Operation System for Nuclear Advanced Robot. This Robot Vision consists of robot position detection, obstacle detection and object recognition. With these vision techniques, a mobile robot can make a path and move autonomously along the planned path. The authors implemented the above robot vision system on the 'Advanced Robot for Nuclear Power Plant' and tested in an environment mocked up as nuclear power plant facilities. Since the operation system for this robot consists of operator's console and a large stereo monitor, this system can be easily operated by one person. Experimental tests were made using the Advanced Robot (nuclear robot). Results indicate that the proposed operation system is very useful, and can be operate by only person. (author)
A cognitive robotic system based on the Soar cognitive architecture for mobile robot navigation, search, and mapping missions

Science.gov (United States)

Hanford, Scott D.

Most unmanned vehicles used for civilian and military applications are remotely operated or are designed for specific applications. As these vehicles are used to perform more difficult missions or a larger number of missions in remote environments, there will be a great need for these vehicles to behave intelligently and autonomously. Cognitive architectures, computer programs that define mechanisms that are important for modeling and generating domain-independent intelligent behavior, have the potential for generating intelligent and autonomous behavior in unmanned vehicles. The research described in this presentation explored the use of the Soar cognitive architecture for cognitive robotics. The Cognitive Robotic System (CRS) has been developed to integrate software systems for motor control and sensor processing with Soar for unmanned vehicle control. The CRS has been tested using two mobile robot missions: outdoor navigation and search in an indoor environment. The use of the CRS for the outdoor navigation mission demonstrated that a Soar agent could autonomously navigate to a specified location while avoiding obstacles, including cul-de-sacs, with only a minimal amount of knowledge about the environment. While most systems use information from maps or long-range perceptual capabilities to avoid cul-de-sacs, a Soar agent in the CRS was able to recognize when a simple approach to avoiding obstacles was unsuccessful and switch to a different strategy for avoiding complex obstacles. During the indoor search mission, the CRS autonomously and intelligently searches a building for an object of interest and common intersection types. While searching the building, the Soar agent builds a topological map of the environment using information about the intersections the CRS detects. The agent uses this topological model (along with Soar's reasoning, planning, and learning mechanisms) to make intelligent decisions about how to effectively search the building. Once the
Teachers' Talk about Robotics: Where Is the Mathematics?

Science.gov (United States)

Savard, Annie; Highfield, Kate

2015-01-01

Programming and the use of robotics present affordances for mathematics learning with application across a broad range of ages. However, realising these affordances in the classroom requires educators to recognise and build apron these potential opportunities for learning. This paper reports one component of a larger study, examining teacher…
Advances in soft computing, intelligent robotics and control

CERN Document Server

Fullér, Robert

2014-01-01

Soft computing, intelligent robotics and control are in the core interest of contemporary engineering. Essential characteristics of soft computing methods are the ability to handle vague information, to apply human-like reasoning, their learning capability, and ease of application. Soft computing techniques are widely applied in the control of dynamic systems, including mobile robots. The present volume is a collection of 20 chapters written by respectable experts of the fields, addressing various theoretical and practical aspects in soft computing, intelligent robotics and control. The first part of the book concerns with issues of intelligent robotics, including robust xed point transformation design, experimental verification of the input-output feedback linearization of differentially driven mobile robot and applying kinematic synthesis to micro electro-mechanical systems design. The second part of the book is devoted to fundamental aspects of soft computing. This includes practical aspects of fuzzy rule ...
Apparatus for multiprocessor-based control of a multiagent robot

Science.gov (United States)

Peters, II, Richard Alan (Inventor)

2009-01-01

An architecture for robot intelligence enables a robot to learn new behaviors and create new behavior sequences autonomously and interact with a dynamically changing environment. Sensory information is mapped onto a Sensory Ego-Sphere (SES) that rapidly identifies important changes in the environment and functions much like short term memory. Behaviors are stored in a DBAM that creates an active map from the robot's current state to a goal state and functions much like long term memory. A dream state converts recent activities stored in the SES and creates or modifies behaviors in the DBAM.
Moving android: on social robots and body-in-interaction.

Science.gov (United States)

Alac, Morana

2009-08-01

Social robotics studies embodied technologies designed for social interaction. This paper examines the implied idea of embodiment using as data a sequence in which practitioners of social robotics are involved in designing a robot's movement. The moments of learning and work in the laboratory enact the social body as material, dynamic, and multiparty: the body-in-interaction. In describing subject-object reconfigurations, the paper explores how the well-known ideas of extending the body with instruments can be applied to a technology designed to function as our surrogate.
Learning plan applicability through active mental entities

International Nuclear Information System (INIS)

Baroni, Pietro; Fogli, Daniela; Guida, Giovanni

1999-01-01

This paper aims at laying down the foundations of a new approach to learning in autonomous mobile robots. It is based on the assumption that robots can be provided with built-in action plans and with mechanisms to modify and improve such plans. This requires that robots are equipped with some form of high-level reasoning capabilities. Therefore, the proposed learning technique is embedded in a novel distributed control architecture featuring an explicit model of robot's cognitive activity. In particular, cognitive activity is obtained by the interaction of active mental entities, such as intentions, persuasions and expectations. Learning capabilities are implemented starting from the interaction of such mental entities. The proposal is illustrated through an example concerning a robot in charge of reaching a target in an unknown environment cluttered with obstacles
Current applications of robotics in spine surgery: a systematic review of the literature.

Science.gov (United States)

Joseph, Jacob R; Smith, Brandon W; Liu, Xilin; Park, Paul

2017-05-01

OBJECTIVE Surgical robotics has demonstrated utility across the spectrum of surgery. Robotics in spine surgery, however, remains in its infancy. Here, the authors systematically review the evidence behind robotic applications in spinal instrumentation. METHODS This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Relevant studies (through October 2016) that reported the use of robotics in spinal instrumentation were identified from a search of the PubMed database. Data regarding the accuracy of screw placement, surgeon learning curve, radiation exposure, and reasons for robotic failure were extracted. RESULTS Twenty-five studies describing 2 unique robots met inclusion criteria. Of these, 22 studies evaluated accuracy of spinal instrumentation. Although grading of pedicle screw accuracy was variable, the most commonly used method was the Gertzbein and Robbins system of classification. In the studies using the Gertzbein and Robbins system, accuracy (Grades A and B) ranged from 85% to 100%. Ten studies evaluated radiation exposure during the procedure. In studies that detailed fluoroscopy usage, overall fluoroscopy times ranged from 1.3 to 34 seconds per screw. Nine studies examined the learning curve for the surgeon, and 12 studies described causes of robotic failure, which included registration failure, soft-tissue hindrance, and lateral skiving of the drill guide. CONCLUSIONS Robotics in spine surgery is an emerging technology that holds promise for future applications. Surgical accuracy in instrumentation implanted using robotics appears to be high. However, the impact of robotics on radiation exposure is not clear and seems to be dependent on technique and robot type.
Teaching Adult Rats Spinalized as Neonates to Walk Using Trunk Robotic Rehabilitation: Elements of Success, Failure, and Dependence.

Science.gov (United States)

Udoekwere, Ubong I; Oza, Chintan S; Giszter, Simon F

2016-08-10

Robot therapy promotes functional recovery after spinal cord injury (SCI) in animal and clinical studies. Trunk actions are important in adult rats spinalized as neonates (NTX rats) that walk autonomously. Quadrupedal robot rehabilitation was tested using an implanted orthosis at the pelvis. Trunk cortical reorganization follows such rehabilitation. Here, we test the functional outcomes of such training. Robot impedance control at the pelvis allowed hindlimb, trunk, and forelimb mechanical interactions. Rats gradually increased weight support. Rats showed significant improvement in hindlimb stepping ability, quadrupedal weight support, and all measures examined. Function in NTX rats both before and after training showed bimodal distributions, with "poor" and "high weight support" groupings. A total of 35% of rats initially classified as "poor" were able to increase their weight-supported step measures to a level considered "high weight support" after robot training, thus moving between weight support groups. Recovered function in these rats persisted on treadmill with the robot both actuated and nonactuated, but returned to pretraining levels if they were completely disconnected from the robot. Locomotor recovery in robot rehabilitation of NTX rats thus likely included context dependence and/or incorporation of models of robot mechanics that became essential parts of their learned strategy. Such learned dependence is likely a hurdle to autonomy to be overcome for many robot locomotor therapies. Notwithstanding these limitations, trunk-based quadrupedal robot rehabilitation helped the rats to visit mechanical states they would never have achieved alone, to learn novel coordinations, and to achieve major improvements in locomotor function. Neonatal spinal transected rats without any weight support can be taught weight support as adults by using robot rehabilitation at trunk. No adult control rats with neonatal spinal transections spontaneously achieve similar changes
A Multidisciplinary Industrial Robot Approach for Teaching Mechatronics-Related Courses

Science.gov (United States)

Garduño-Aparicio, Mariano; Rodríguez-Reséndiz, Juvenal; Macias-Bobadilla, Gonzalo; Thenozhi, Suresh

2018-01-01

This paper presents a robot prototype for an undergraduate laboratory program designed to fulfill the criteria laid out by ABET. The main objective of the program is for students to learn some basic concepts of embedded systems and robotics, and apply them in practice. For that purpose, various practical laboratory exercises were prepared to teach…
A randomized study of the immunogenicity and safety of Japanese encephalitis chimeric virus vaccine (JE-CV) in comparison with SA14-14-2 vaccine in children in the Republic of Korea.

Science.gov (United States)

Kim, Dong Soo; Houillon, Guy; Jang, Gwang Cheon; Cha, Sung-Ho; Choi, Soo-Han; Lee, Jin; Kim, Hwang Min; Kim, Ji Hong; Kang, Jin Han; Kim, Jong-Hyun; Kim, Ki Hwan; Kim, Hee Soo; Bang, Joon; Naimi, Zulaikha; Bosch-Castells, Valérie; Boaz, Mark; Bouckenooghe, Alain

2014-01-01

A new live attenuated Japanese encephalitis chimeric virus vaccine (JE-CV) has been developed based on innovative technology to give protection against JE with an improved immunogenicity and safety profile. In this phase 3, observer-blind study, 274 children aged 12-24 months were randomized 1:1 to receive one dose of JE-CV (Group JE-CV) or the SA14-14-2 vaccine currently used to vaccinate against JE in the Republic of Korea (Group SA14-14-2). JE neutralizing antibody titers were assessed using PRNT50 before and 28 days after vaccination. The primary endpoint of non-inferiority of seroconversion rates on D28 was demonstrated in the Per Protocol analysis set as the difference between Group JE-CV and Group SA14-14-2 was 0.9 percentage points (95% confidence interval [CI]: -2.35; 4.68), which was above the required -10%. Seroconversion and seroprotection rates 28 days after administration of a single vaccine dose were 100% in Group JE-CV and 99.1% in Group SA14-14-2; all children except one (Group SA14-14-2) were seroprotected. Geometric mean titers (GMTs) increased in both groups from D0 to D28; GM of titer ratios were slightly higher in Group JE-CV (182 [95% CI: 131; 251]) than Group SA14-14-2 (116 [95% CI: 85.5, 157]). A single dose of JE-CV was well tolerated and no safety concerns were identified. In conclusion, a single dose of JE-CV or SA14-14-2 vaccine elicited a comparable immune response with a good safety profile. Results obtained in healthy Korean children aged 12-24 months vaccinated with JE-CV are consistent with those obtained in previous studies conducted with JE-CV in toddlers.
Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems

Directory of Open Access Journals (Sweden)

Elmar eRückert

2013-10-01

Full Text Available A salient feature of human motor skill learning is the ability to exploitsimilarities across related tasks.In biological motor control, it has been hypothesized that muscle synergies,coherent activations of groups of muscles, allow for exploiting shared knowledge.Recent studies have shown that a rich set of complex motor skills can be generated bya combination of a small number of muscle synergies.In robotics, dynamic movement primitives are commonlyused for motor skill learning. This machine learning approach implements a stable attractor systemthat facilitates learning and it can be used in high-dimensional continuous spaces. However, it does not allow for reusing shared knowledge, i.e. for each task an individual set of parameters has to be learned.We propose a novel movement primitive representationthat employs parametrized basis functions, which combines the benefits of muscle synergiesand dynamic movement primitives. For each task asuperposition of synergies modulates a stable attractor system.This approach leads to a compact representation of multiple motor skills andat the same time enables efficient learning in high-dimensional continuous systems.The movement representation supports discrete and rhythmic movements andin particular includes the dynamic movement primitive approach as a special case.We demonstrate the feasibility of the movement representation in three multi-task learning simulated scenarios.First, the characteristics of the proposed representation are illustrated in a point-mass task.Second, in complex humanoid walking experiments,multiple walking patterns with different step heights are learned robustly and efficiently.Finally, in a multi-directional reaching task simulated with a musculoskeletal modelof the human arm, we show how the proposed movement primitives can be used tolearn appropriate muscle excitation patterns and to generalize effectively to new reaching skills.
Social Robotics in Therapy of Apraxia of Speech

Directory of Open Access Journals (Sweden)

José Carlos Castillo

2018-01-01

Full Text Available Apraxia of speech is a motor speech disorder in which messages from the brain to the mouth are disrupted, resulting in an inability for moving lips or tongue to the right place to pronounce sounds correctly. Current therapies for this condition involve a therapist that in one-on-one sessions conducts the exercises. Our aim is to work in the line of robotic therapies in which a robot is able to perform partially or autonomously a therapy session, endowing a social robot with the ability of assisting therapists in apraxia of speech rehabilitation exercises. Therefore, we integrate computer vision and machine learning techniques to detect the mouth pose of the user and, on top of that, our social robot performs autonomously the different steps of the therapy using multimodal interaction.
Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control †

Directory of Open Access Journals (Sweden)

René Felix Reinhart

2017-02-01

Full Text Available Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant’s intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms.
Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control †

Science.gov (United States)

Reinhart, René Felix; Shareef, Zeeshan; Steil, Jochen Jakob

2017-01-01

Feed-forward model-based control relies on models of the controlled plant, e.g., in robotics on accurate knowledge of manipulator kinematics or dynamics. However, mechanical and analytical models do not capture all aspects of a plant’s intrinsic properties and there remain unmodeled dynamics due to varying parameters, unmodeled friction or soft materials. In this context, machine learning is an alternative suitable technique to extract non-linear plant models from data. However, fully data-based models suffer from inaccuracies as well and are inefficient if they include learning of well known analytical models. This paper thus argues that feed-forward control based on hybrid models comprising an analytical model and a learned error model can significantly improve modeling accuracy. Hybrid modeling here serves the purpose to combine the best of the two modeling worlds. The hybrid modeling methodology is described and the approach is demonstrated for two typical problems in robotics, i.e., inverse kinematics control and computed torque control. The former is performed for a redundant soft robot and the latter for a rigid industrial robot with redundant degrees of freedom, where a complete analytical model is not available for any of the platforms. PMID:28208697
Apskaitos politikos formavimo įmonėje tyrimai

OpenAIRE

Vaičiulytė-Petrauskienė, Edita

2009-01-01

Tyrimo objektas – apskaitos politikos formavimas įmonėje. Darbo tikslas – nustačius veiksnius, sąlygojančius apskaitos politikos pasirinkimą, sudaryti apskaitos politikos pasirinkimo pelno atžvilgiu modelį ir patikrinti jo tinkamumą Lietuvos įmonėse. Uždaviniai: 1) nustatyti pelną didinančios/mažinančios apskaitos politikos pasirinkimą sąlygojančius veiksnius; 2) išskirti ir nustatyti pelną didinančius ir pelną mažinančius apskaitos metodus, kuriuos pasirinkdamos įmonės formuoja savo apskaito...
First 101 Robotic General Surgery Cases in a Community Hospital

Science.gov (United States)

Robertson, Jarrod C.; Alrajhi, Sharifah

2016-01-01

Background and Objectives: The general surgeon's robotic learning curve may improve if the experience is classified into categories based on the complexity of the procedures in a small community hospital. The intraoperative time should decrease and the incidence of complications should be comparable to conventional laparoscopy. The learning curve of a single robotic general surgeon in a small community hospital using the da Vinci S platform was analyzed. Methods: Measured parameters were operative time, console time, conversion rates, complications, surgical site infections (SSIs), surgical site occurrences (SSOs), length of stay, and patient demographics. Results: Between March 2014 and August 2015, 101 robotic general surgery cases were performed by a single surgeon in a 266-bed community hospital, including laparoscopic cholecystectomies, inguinal hernia repairs; ventral, incisional, and umbilical hernia repairs; and colorectal, foregut, bariatric, and miscellaneous procedures. Ninety-nine of the cases were completed robotically. Seven patients were readmitted within 30 days. There were 8 complications (7.92%). There were no mortalities and all complications were resolved with good outcomes. The mean operative time was 233.0 minutes. The mean console operative time was 117.6 minutes. Conclusion: A robotic general surgery program can be safely implemented in a small community hospital with extensive training of the surgical team through basic robotic skills courses as well as supplemental educational experiences. Although the use of the robotic platform in general surgery could be limited to complex procedures such as foregut and colorectal surgery, it can also be safely used in a large variety of operations with results similar to those of conventional laparoscopy. PMID:27667913
Dynamics and control of robot for capturing objects in space

Science.gov (United States)

Huang, Panfeng

. After capturing the object, the space robot must complete the following two tasks: one is to berth the object, and the other is to re-orientate the attitude of the whole robot system for communication and power supply. Therefore, I propose a method to accomplish these two tasks simultaneously using manipulator motion only. The ultimate goal of space services is to realize the capture and manipulation autonomously. Therefore, I propose an affective approach based on learning human skill to track and capture the objects automatically in space. With human-teaching demonstration, the space robot is able to learn and abstract human tracking and capturing skill using an efficient neural-network learning architecture that combines flexible Cascade Neural Networks with Node Decoupled Extended Kalman Filtering (CNN-NDEKF). The simulation results attest that this approach is useful and feasible in tracking trajectory planning and capturing of space robot. Finally I propose a novel approach based on Genetic Algorithms (GAs) to optimize the approach trajectory of space robots in order to realize effective and stable operations. I complete the minimum-torque path planning in order to save the limited energy in space, and design the minimum jerk trajectory for the stabilization of the space manipulator and its space base. These optimal algorithms are very important and useful for the application of space robot.

Deepening Learning through Learning-by-Inventing

OpenAIRE

Apiola, Mikko; Tedre, Matti

2013-01-01

It has been shown that deep approaches to learning, intrinsic motivation, and self-regulated learning have strong positive effects on learning. How those pedagogical theories can be integrated in computing curricula is, however, still lacking empirically grounded analyses. This study integrated, in a robotics-based programming class, a method of learning-by-inventing, and studied its qualitative effects on students’ learning through 144 interviews. Five findings were related with learning the...
Is the geopotential directly measurable (Gauss, Bruns, Einstein : Je li geopotencijal direktno mjerljiv? (Gaus, Bruns, Ajnštajn

Directory of Open Access Journals (Sweden)

Helmut Moritz

2016-12-01

Full Text Available It has been pointed out by the great Swedish geodesist Arne Bjerhammar and others around1985 that it is possible to replace the classical method of spirit leveling for determining differences of the geopotential by a much more direct and elegant method, measuring the frequency of atomic clocks. This is impossible by classical physics and requires methods of Einstein’s General Theory of Relativity. The principle is that the geopotential can be “felt” by the “proper time” of this theory, but there remained the problem that the measuring accuracies were unthinkably high in 1985 and even later. To get a leveling accuracy of 1 cm, we must measure these frequencies to a relative accuracy of 10-18. Reaching such accuracies provided a great challenge to high-precision time observation all over the world, from USA to China. Now it seems that the required frequency accuracy is being reached. The author tries to give a short introductory review accessible to geodetic students and surveyors. It is purely didactic. : Veliki švedski geodeta Arne Bjerhammar (i neki drugi, istakao je oko 1985. godine, da je klasičnu metodu geometrijskog nivelmana za određivanje razlika geopotencijala moguće zamijeniti mnogo direktnijom i elegantnom metodom, mjerenjem frekvencije atomskih satova. Ovo nije moguće metodama klasične fizike, te zahtijeva primjenu Ajnštajnove Teorije općeg relativiteta. Princip je da se geopotencijal može “osjetiti” pomoću “pravog vremena” ove teorije, ali ostaje problem što je tačnost mjerenja bila nezamisliva u 1985. godini, pa čak i poslije. Da bi se dobila tačnost nivelanja od 1 cm, frekvencije se moraju mjeriti s relativnom tačnošću od 10-18. Dostizanje ove tačnosti bio je ogroman izazov za sve svjetske opservatorije za visokoprecizno mjerenje vremena, od SAD do Kine. Čini se da je zahtijevana tačnost ipak dostignuta. Autor nastoji dati kratak, potpuno didaktički uvod, pristupačan studentima geodezije i
Initial phases of design-based research into the educational potentials of NAO-robots

DEFF Research Database (Denmark)

Majgaard, Gunver; Bertel, Lykke Brogaard

2014-01-01

In this paper, we describe our initial research, using the humanoid robot NAO in primary and secondary schools. How does a programmable humanoid enrich teaching and how do we prepare the teachers? Ten school classes are using the robot for creative programming. So far we have experienced...... that the robot enriches the learning processes by combining the auditory, visual and kinaesthetic modalities....
Robotics

Science.gov (United States)

Popov, E. P.; Iurevich, E. I.

The history and the current status of robotics are reviewed, as are the design, operation, and principal applications of industrial robots. Attention is given to programmable robots, robots with adaptive control and elements of artificial intelligence, and remotely controlled robots. The applications of robots discussed include mechanical engineering, cargo handling during transportation and storage, mining, and metallurgy. The future prospects of robotics are briefly outlined.
Cooperative robots and sensor networks

CERN Document Server

Khelil, Abdelmajid

2014-01-01

Mobile robots and Wireless Sensor Networks (WSNs) have enabled great potentials and a large space for ubiquitous and pervasive applications. Robotics and WSNs have mostly been considered as separate research fields and little work has investigated the marriage between these two technologies. However, these two technologies share several features, enable common cyber-physical applications and provide complementary support to each other.  The primary objective of book is to provide a reference for cutting-edge studies and research trends pertaining to robotics and sensor networks, and in particular for the coupling between them. The book consists of five chapters. The first chapter presents a cooperation strategy for teams of multiple autonomous vehicles to solve the rendezvous problem. The second chapter is motivated by the need to improve existing solutions that deal with connectivity prediction, and proposed a genetic machine learning approach for link-quality prediction. The third chapter presents an arch...
Na velikosti nezáleží - je to fraktální

Czech Academy of Sciences Publication Activity Database

Řípa, Milan

2014-01-01

Roč. 7, červen (2014) Institutional support: RVO:61389021 Keywords : fusion * tokamak * fractal * turbulence * Bohm diffusion Subject RIV: BL - Plasma and Gas Discharge Physics http://3pol.cz/1614-na-velikosti-nezalezi-je-to-fraktalni-aneb-slunecni-termojaderny-reaktor
Hexapod Robot

Science.gov (United States)

Begody, Ericka

2016-01-01

right or up and down. The hexapod will eventually be able to track the object moving its head and body in sync with on another and being able to rotate its body at 360 degrees. This is the plans and possible end results for the hexapod robot I will be working on during my summer internship at NASA Johnson Space Center. Since working on the hexapod project I have gained an increase interest in robotics. I enjoy the process of critical thinking. Also will working on this project I was challenged in a way that made more passionate to strive even more to become an engineer. I've learned that asking questions is an important part of the learning process. Also I learn that much more is accomplished when teamwork is applied.
Towards Machine Learning of Motor Skills

Science.gov (United States)

Peters, Jan; Schaal, Stefan; Schölkopf, Bernhard

Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and cognitive sciences. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s, however, made it clear that an approach purely based on reasoning or human insights would not be able to model all the perceptuomotor tasks that a robot should fulfill. Instead, new hope was put in the growing wake of machine learning that promised fully adaptive control algorithms which learn both by observation and trial-and-error. However, to date, learning techniques have yet to fulfill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics, and usually scaling was only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general approach to motor skill learning in order to get one step closer towards human-like performance. For doing so, we study two major components for such an approach, i.e., firstly, a theoretically well-founded general approach to representing the required control structures for task representation and execution and, secondly, appropriate learning algorithms which can be applied in this setting.
Robot 2015 : Second Iberian Robotics Conference : Advances in Robotics

CERN Document Server

Moreira, António; Lima, Pedro; Montano, Luis; Muñoz-Martinez, Victor

2016-01-01

This book contains a selection of papers accepted for presentation and discussion at ROBOT 2015: Second Iberian Robotics Conference, held in Lisbon, Portugal, November 19th-21th, 2015. ROBOT 2015 is part of a series of conferences that are a joint organization of SPR – “Sociedade Portuguesa de Robótica/ Portuguese Society for Robotics”, SEIDROB – Sociedad Española para la Investigación y Desarrollo de la Robótica/ Spanish Society for Research and Development in Robotics and CEA-GTRob – Grupo Temático de Robótica/ Robotics Thematic Group. The conference organization had also the collaboration of several universities and research institutes, including: University of Minho, University of Porto, University of Lisbon, Polytechnic Institute of Porto, University of Aveiro, University of Zaragoza, University of Malaga, LIACC, INESC-TEC and LARSyS. Robot 2015 was focussed on the Robotics scientific and technological activities in the Iberian Peninsula, although open to research and delegates from other...
Innovative Mobile Robot Method: Improving the Learning of Programming Languages in Engineering Degrees

Science.gov (United States)

Ortiz, Octavio Ortiz; Pastor Franco, Juan Ángel; Alcover Garau, Pedro María; Herrero Martín, Ruth

2017-01-01

This paper describes a study of teaching a programming language in a C programming course by having students assemble and program a low-cost mobile robot. Writing their own programs to define the robot's behavior raised students' motivation. Working in small groups, students programmed the robots by using the control structures of structured…
Use of robotics as a learning aid for disabled children

Directory of Open Access Journals (Sweden)

Teodiano Freire Bastos

2012-05-01

Full Text Available Severe disabled children have little chance of environmental and social exploration and discovery, and due to this lack of interaction and independency, it may lead to an idea that they are unable to do anything by themselves. Trying to help these children on this situation, educational robotics can offer and aid, once it can give them a certain degree of independency in exploration of environment. The system developed in this work allows the child to transmit the commands to a robot. Sensors placed on the child’s body can obtain information from head movement or muscle signals to command the robot to carry out tasks. With the use of this system, the disabled children get a better cognitive development and social interaction, balancing in a certain way, the negative effects of their disabilities.
Adapting a robotics program to enhance participation and interest in STEM among children with disabilities: a pilot study.

Science.gov (United States)

Lindsay, Sally; Hounsell, Kara Grace

2017-10-01

Youth with disabilities are under-represented in science, technology, engineering, and math (STEM) in school and in the workforce. One encouraging approach to engage youth's interest in STEM is through robotics; however, such programs are mostly for typically developing youth. The purpose of this study was to understand the development and implementation of an adapted robotics program for children and youth with disabilities and their experiences within it. Our mixed methods pilot study (pre- and post-workshop surveys, observations, and interviews) involved 41 participants including: 18 youth (aged 6-13), 12 parents and 11 key informants. The robotics program involved 6, two-hour workshops held at a paediatric hospital. Our findings showed that several adaptations made to the robotics program helped to enhance the participation of children with disabilities. Adaptations addressed the educational/curriculum, cognitive and learning, physical and social needs of the children. In regards to experiences within the adapted hospital program, our findings highlight that children enjoyed the program and learned about computer programming and building robots. Clinicians and educators should consider engaging youth with disabilities in robotics to enhance learning and interest in STEM. Implications for Rehabilitation Clinicians and educators should consider adapting curriculum content and mode of delivery of LEGO ® robotics programs to include youth with disabilities. Appropriate staffing including clinicians and educators who are knowledgeable about youth with disabilities and LEGO ® robotics are needed. Clinicians should consider engaging youth with disabilities in LEGO ® to enhance learning and interest in STEM.
Incremental active learning of sensorimotor models in developmental robotics

OpenAIRE

Ribes Sanz, Arturo

2015-01-01

La rápida evolución de la robótica esta promoviendo que emerjan nuevos campos relacionados con la robótica. Inspirándose en ideas provinientes de la psicología del desarrollo, la robótica del desarrollo es un nuevo campo que pretende proveer a los robots de capacidades que les permiten aprender de una manera abierta durante toda su vida. Hay situaciones donde los ingenieros o los diseñadores no pueden prever todos los posibles problemas que un robot pueda encontrar. Tal como el número de tare...
Field dose radiation determination by active learning with Gaussian Process for autonomous robot guiding

International Nuclear Information System (INIS)

Freitas Naiff, Danilo de; Silveira, Paulo R.; Pereira, Claudio M.N.A.

2017-01-01

This article proposes an approach for determination of radiation dose pro le in a radiation-susceptible environment, aiming to guide an autonomous robot in acting on those environments, reducing the human exposure to dangerous amount of dose. The approach consists of an active learning method based on information entropy reduction, using log-normally warped Gaussian Process (GP) as surrogate model, resulting in non-linear online regression with sequential measurements. Experiments with simulated radiation dose fields of varying complexity were made, and results showed that the approach was effective in reconstruct the eld with high accuracy, through relatively few measurements. The technique was also shown some robustness in presence measurement noise, present in real measurements, by assuming Gaussian noise. (author)
Field dose radiation determination by active learning with Gaussian Process for autonomous robot guiding

Energy Technology Data Exchange (ETDEWEB)

Freitas Naiff, Danilo de; Silveira, Paulo R.; Pereira, Claudio M.N.A., E-mail: danilonai1992@poli.ufrj.br, E-mail: paulo@lmp.ufrj.br, E-mail: cmnap@ien.gov.br [Coordenacao de Pos-Graduacao e Pesquisa de Engenharia (PEN/COPPE/UFRJ), Rio de Janeiro, RJ (Brazil); Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)

2017-11-01

This article proposes an approach for determination of radiation dose pro le in a radiation-susceptible environment, aiming to guide an autonomous robot in acting on those environments, reducing the human exposure to dangerous amount of dose. The approach consists of an active learning method based on information entropy reduction, using log-normally warped Gaussian Process (GP) as surrogate model, resulting in non-linear online regression with sequential measurements. Experiments with simulated radiation dose fields of varying complexity were made, and results showed that the approach was effective in reconstruct the eld with high accuracy, through relatively few measurements. The technique was also shown some robustness in presence measurement noise, present in real measurements, by assuming Gaussian noise. (author)
Human-assisted sound event recognition for home service robots.

Science.gov (United States)

Do, Ha Manh; Sheng, Weihua; Liu, Meiqin

This paper proposes and implements an open framework of active auditory learning for a home service robot to serve the elderly living alone at home. The framework was developed to realize the various auditory perception capabilities while enabling a remote human operator to involve in the sound event recognition process for elderly care. The home service robot is able to estimate the sound source position and collaborate with the human operator in sound event recognition while protecting the privacy of the elderly. Our experimental results validated the proposed framework and evaluated auditory perception capabilities and human-robot collaboration in sound event recognition.
Learning tactile skills through curious exploration

Directory of Open Access Journals (Sweden)

Leo ePape

2012-07-01

Full Text Available We present curiosity-driven, autonomous acquisition of tactile exploratory skills on a biomimetic robot finger equipped with an array of microelectromechanical touch sensors. Instead of building tailored algorithms for solving a specific tactile task, we employ a more general curiosity-driven reinforcement learning approach that autonomously learns a set of motor skills in absence of an explicit teacher signal. In this approach, the acquisition of skills is driven by the information content of the sensory input signals relative to a learner that aims at representing sensory inputs using fewer and fewer computational resources. We show that, from initially random exploration of its environment, the robotic system autonomously develops a small set of basic motor skills that lead to different kinds of tactile input. Next, the system learns how to exploit the learned motor skills to solve supervised texture classification tasks. Our approach demonstrates the feasibility of autonomous acquisition of tactile skills on physical robotic platforms through curiosity-driven reinforcement learning, overcomes typical difficulties of engineered solutions for active tactile exploration and underactuated control, and provides a basis for studying developmental learning through intrinsic motivation in robots.
Robot Actors, Robot Dramaturgies

DEFF Research Database (Denmark)

Jochum, Elizabeth

This paper considers the use of tele-operated robots in live performance. Robots and performance have long been linked, from the working androids and automata staged in popular exhibitions during the nineteenth century and the robots featured at Cybernetic Serendipity (1968) and the World Expo...
Exploring the Possibility of Using Humanoid Robots as Instructional Tools for Teaching a Second Language in Primary School

Science.gov (United States)

Chang, Chih-Wei; Lee, Jih-Hsien; Chao, Po-Yao; Wang, Chin-Yeh; Chen, Gwo-Dong

2010-01-01

As robot technologies develop, many researchers have tried to use robots to support education. Studies have shown that robots can help students develop problem-solving abilities and learn computer programming, mathematics, and science. However, few studies discuss the use of robots to facilitate the teaching of second languages. We discuss whether…
Learning-based adaptive prescribed performance control of postcapture space robot-target combination without inertia identifications

Science.gov (United States)

Wei, Caisheng; Luo, Jianjun; Dai, Honghua; Bian, Zilin; Yuan, Jianping

2018-05-01

In this paper, a novel learning-based adaptive attitude takeover control method is investigated for the postcapture space robot-target combination with guaranteed prescribed performance in the presence of unknown inertial properties and external disturbance. First, a new static prescribed performance controller is developed to guarantee that all the involved attitude tracking errors are uniformly ultimately bounded by quantitatively characterizing the transient and steady-state performance of the combination. Then, a learning-based supplementary adaptive strategy based on adaptive dynamic programming is introduced to improve the tracking performance of static controller in terms of robustness and adaptiveness only utilizing the input/output data of the combination. Compared with the existing works, the prominent advantage is that the unknown inertial properties are not required to identify in the development of learning-based adaptive control law, which dramatically decreases the complexity and difficulty of the relevant controller design. Moreover, the transient and steady-state performance is guaranteed a priori by designer-specialized performance functions without resorting to repeated regulations of the controller parameters. Finally, the three groups of illustrative examples are employed to verify the effectiveness of the proposed control method.

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