Robot Learning
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Robot Learning From Human Teachers
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Author : Sonia Chernova
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2014-04-01
Robot Learning From Human Teachers written by Sonia Chernova and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-01 with Computers categories.
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 from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system. Chapter 2 gives a brief survey of the psychology literature that provides insights from human social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstrations. Next, is the choice of modeling technique. Currently, there is a dichotomy in the field between approaches that model low-level motor skills and those that model high-level tasks composed of primitive actions. We devote a chapter to each of these. Chapter 7 is devoted to interactive and active learning approaches that allow the robot to refine an existing task model. And finally, Chapter 8 provides best practices for evaluation of LfD systems, with a focus on how to approach experiments with human subjects in this domain.
Robot Learning
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Author : J. H. Connell
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Robot Learning written by J. H. Connell and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
Building a robot that learns to perform a task has been acknowledged as one of the major challenges facing artificial intelligence. Self-improving robots would relieve humans from much of the drudgery of programming and would potentially allow operation in environments that were changeable or only partially known. Progress towards this goal would also make fundamental contributions to artificial intelligence by furthering our understanding of how to successfully integrate disparate abilities such as perception, planning, learning and action. Although its roots can be traced back to the late fifties, the area of robot learning has lately seen a resurgence of interest. The flurry of interest in robot learning has partly been fueled by exciting new work in the areas of reinforcement earning, behavior-based architectures, genetic algorithms, neural networks and the study of artificial life. Robot Learning gives an overview of some of the current research projects in robot learning being carried out at leading universities and research laboratories in the United States. The main research directions in robot learning covered in this book include: reinforcement learning, behavior-based architectures, neural networks, map learning, action models, navigation and guided exploration.
Recent Advances In Robot Learning
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Author : Judy A. Franklin
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Recent Advances In Robot Learning written by Judy A. Franklin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Recent Advances in Robot Learning contains seven papers on robot learning written by leading researchers in the field. As the selection of papers illustrates, the field of robot learning is both active and diverse. A variety of machine learning methods, ranging from inductive logic programming to reinforcement learning, is being applied to many subproblems in robot perception and control, often with objectives as diverse as parameter calibration and concept formulation. While no unified robot learning framework has yet emerged to cover the variety of problems and approaches described in these papers and other publications, a clear set of shared issues underlies many robot learning problems. Machine learning, when applied to robotics, is situated: it is embedded into a real-world system that tightly integrates perception, decision making and execution. Since robot learning involves decision making, there is an inherent active learning issue. Robotic domains are usually complex, yet the expense of using actual robotic hardware often prohibits the collection of large amounts of training data. Most robotic systems are real-time systems. Decisions must be made within critical or practical time constraints. These characteristics present challenges and constraints to the learning system. Since these characteristics are shared by other important real-world application domains, robotics is a highly attractive area for research on machine learning. On the other hand, machine learning is also highly attractive to robotics. There is a great variety of open problems in robotics that defy a static, hand-coded solution. Recent Advances in Robot Learning is an edited volume of peer-reviewed original research comprising seven invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 23, Numbers 2 and 3).
Interdisciplinary Approaches To Robot Learning
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Author : J Demiris
language : en
Publisher: World Scientific
Release Date : 2000-06-12
Interdisciplinary Approaches To Robot Learning written by J Demiris and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-06-12 with Technology & Engineering categories.
Robots are being used in increasingly complicated and demanding tasks, often in environments that are complex or even hostile. Underwater, space and volcano exploration are just some of the activities that robots are taking part in, mainly because the environments that are being explored are dangerous for humans. Robots can also inhabit dynamic environments, for example to operate among humans, not just in factories, but also taking on more active roles. Recently, for instance, they have made their way into the home entertainment market. Given the variety of situations that robots will be placed in, learning becomes increasingly important. Robot learning is essentially about equipping robots with the capacity to improve their behaviour over time, based on their incoming experiences. The papers in this volume present a variety of techniques. Each paper provides a mini-introduction to a subfield of robot learning. Some also give a fine introduction to the field of robot learning as a whole. There is one unifying aspect to the work reported in the book, namely its interdisciplinary nature, especially in the combination of robotics, computer science and biology. This approach has two important benefits: first, the study of learning in biological systems can provide robot learning scientists and engineers with valuable insights into learning mechanisms of proven functionality and versatility; second, computational models of learning in biological systems, and their implementation in simulated agents and robots, can provide researchers of biological systems with a powerful platform for the development and testing of learning theories. Contents:Interdisciplinary Approaches to Robot Learning: Introduction (J Demiris & A Birk)Bootstrapping the Developmental Process: The Filter Hypothesis (L Berthouze)Biomimetic Gaze Stabilization (T Shibata & S Schaal)Experiments and Models About Cognitive Map Learning for Motivated Navigation (P Gaussier et al.)Learning Selection of Action for Cortically-Inspired Robot Control (H Frezza-Buet & F Alexandre)Transferring Learned Knowledge in a Lifelong Learning Mobile Robot Agent (J O'Sullivan)Of Hummingbirds and Helicopters: An Algebraic Framework for Interdisciplinary Studies of Imitation and Its Applications (C Nehaniv & K Dautenhahn)Evolving Complex Visual Behaviours Using Genetic Programming and Shaping (S Perkins & G M Hayes)Preston: A System for the Evaluation of Behaviour Sequences (M Wilson) Readership: Researchers and graduate students in robotics and machine learning who are interested in interdisciplinary approaches to their fields. Keywords:Robotics;Learning, Interdisciplinary Approaches;Robot Learning;Robots;Adaptivity;Biologically Inspired Robotics
Robot Learning Human Skills And Intelligent Control Design
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Author : Chenguang Yang
language : en
Publisher: CRC Press
Release Date : 2021-06-21
Robot Learning Human Skills And Intelligent Control Design written by Chenguang Yang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-21 with Computers categories.
In the last decades robots are expected to be of increasing intelligence to deal with a large range of tasks. Especially, robots are supposed to be able to learn manipulation skills from humans. To this end, a number of learning algorithms and techniques have been developed and successfully implemented for various robotic tasks. Among these methods, learning from demonstrations (LfD) enables robots to effectively and efficiently acquire skills by learning from human demonstrators, such that a robot can be quickly programmed to perform a new task. This book introduces recent results on the development of advanced LfD-based learning and control approaches to improve the robot dexterous manipulation. First, there's an introduction to the simulation tools and robot platforms used in the authors' research. In order to enable a robot learning of human-like adaptive skills, the book explains how to transfer a human user’s arm variable stiffness to the robot, based on the online estimation from the muscle electromyography (EMG). Next, the motion and impedance profiles can be both modelled by dynamical movement primitives such that both of them can be planned and generalized for new tasks. Furthermore, the book introduces how to learn the correlation between signals collected from demonstration, i.e., motion trajectory, stiffness profile estimated from EMG and interaction force, using statistical models such as hidden semi-Markov model and Gaussian Mixture Regression. Several widely used human-robot interaction interfaces (such as motion capture-based teleoperation) are presented, which allow a human user to interact with a robot and transfer movements to it in both simulation and real-word environments. Finally, improved performance of robot manipulation resulted from neural network enhanced control strategies is presented. A large number of examples of simulation and experiments of daily life tasks are included in this book to facilitate better understanding of the readers.
Advances In Robot Learning
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Author : Jeremy Wyatt
language : en
Publisher: Springer
Release Date : 2003-06-29
Advances In Robot Learning written by Jeremy Wyatt and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-29 with Computers categories.
This book constitutes the thoroughly refereed post-workshop proceedings of the 8th European Workshop on Learning Robots, EWLR'99, held in Lausanne, Switzerland in September 1999.The seven revised full workshop papers presented were carefully reviewed and selected for inclusion in the book. Also included are two invited full papers. Among the topics addressed are map building for robot navigation, multi-task reinforcement learning, neural network approaches, example-based learning, situated agents, planning maps for mobile robots, path finding, autonomous robots, and biologically inspired approaches.
Robot Learning By Visual Observation
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Author : Aleksandar Vakanski
language : en
Publisher: John Wiley & Sons
Release Date : 2017-02-13
Robot Learning By Visual Observation written by Aleksandar Vakanski and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-13 with Technology & Engineering categories.
This book presents programming by demonstration for robot learning from observations with a focus on the trajectory level of task abstraction Discusses methods for optimization of task reproduction, such as reformulation of task planning as a constrained optimization problem Focuses on regression approaches, such as Gaussian mixture regression, spline regression, and locally weighted regression Concentrates on the use of vision sensors for capturing motions and actions during task demonstration by a human task expert
Language And Learning For Robots
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Author : Colleen Crangle
language : en
Publisher: Center for the Study of Language (CSLI)
Release Date : 1994
Language And Learning For Robots written by Colleen Crangle and has been published by Center for the Study of Language (CSLI) this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Computers categories.
Robot technology will find wide-scale use only when a robotic device can be given commands and taught new tasks in a natural language. How could a robot understand instructions expressed in English? How could a robot learn from instructions? Crangle and Suppes begin to answer these questions through a theoretical approach to language and learning for robots, and by experimental work with robots. The authors develop the notion of an instructable robot - one which derives its intelligence in part from interaction with humans. Since verbal interaction with a robot requires a natural language semantics, the authors propose a natural-model semantics which they then apply to the interpretation of robot commands. Two experimental projects are described which provide natural-language interfaces to robotic aids for the physically disabled.
From Motor Learning To Interaction Learning In Robots
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Author : Olivier Sigaud
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-02-04
From Motor Learning To Interaction Learning In Robots written by Olivier Sigaud and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-02-04 with Computers categories.
From an engineering standpoint, the increasing complexity of robotic systems and the increasing demand for more autonomously learning robots, has become essential. This book is largely based on the successful workshop “From motor to interaction learning in robots” held at the IEEE/RSJ International Conference on Intelligent Robot Systems. The major aim of the book is to give students interested the topics described above a chance to get started faster and researchers a helpful compandium.
Robots For Kids
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Author : Allison Druin
language : en
Publisher: Morgan Kaufmann
Release Date : 2000
Robots For Kids written by Allison Druin and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Computers categories.
This work brings together the insights of ten designers, researchers, and educators, each invited to contribute a chapter that relates his or her experience develping or using a children's robotic learning device. This growing area of endeavour is expected to have prodound and long-lasting effets on the ways children learn and develop, and its participants come from a wide range of backgrounds.