Author: Spyridon KarachaliosPublisher:
ISBN:
Category :
Languages : en
Pages : 90
Book Description
Human-robot Interaction Learning Using Programming by Demonstration and Reinforcement Learning
Author: Spyridon KarachaliosRobot Learning from Human Teachers
Author: Sonia ChernovaPublisher: Morgan & Claypool Publishers
ISBN: 1681731797
Category : Computers
Languages : en
Pages : 165
Book Description
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.
Human-Robot Interaction Control Using Reinforcement Learning
Author: Wen YuPublisher: John Wiley & Sons
ISBN: 1119782740
Category : Technology & Engineering
Languages : en
Pages : 290
Book Description
A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement Learning, an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation. Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control. The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics. Readers will also enjoy: A thorough introduction to model-based human-robot interaction control Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning Perfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning.
Robot Programming by Demonstration
Author: Sylvain CalinonPublisher: EPFL Press
ISBN: 9781439808672
Category : Computers
Languages : en
Pages : 248
Book Description
Recent advances in RbD have identified a number of key issues for ensuring a generic approach to the transfer of skills across various agents and contexts. This book focuses on the two generic questions of what to imitate and how to imitate and proposes active teaching methods.
Springer Handbook of Robotics
Author: Bruno SicilianoPublisher: Springer
ISBN: 3319325523
Category : Technology & Engineering
Languages : en
Pages : 2259
Book Description
The second edition of this handbook provides a state-of-the-art overview on the various aspects in the rapidly developing field of robotics. Reaching for the human frontier, robotics is vigorously engaged in the growing challenges of new emerging domains. Interacting, exploring, and working with humans, the new generation of robots will increasingly touch people and their lives. The credible prospect of practical robots among humans is the result of the scientific endeavour of a half a century of robotic developments that established robotics as a modern scientific discipline. The ongoing vibrant expansion and strong growth of the field during the last decade has fueled this second edition of the Springer Handbook of Robotics. The first edition of the handbook soon became a landmark in robotics publishing and won the American Association of Publishers PROSE Award for Excellence in Physical Sciences & Mathematics as well as the organization’s Award for Engineering & Technology. The second edition of the handbook, edited by two internationally renowned scientists with the support of an outstanding team of seven part editors and more than 200 authors, continues to be an authoritative reference for robotics researchers, newcomers to the field, and scholars from related disciplines. The contents have been restructured to achieve four main objectives: the enlargement of foundational topics for robotics, the enlightenment of design of various types of robotic systems, the extension of the treatment on robots moving in the environment, and the enrichment of advanced robotics applications. Further to an extensive update, fifteen new chapters have been introduced on emerging topics, and a new generation of authors have joined the handbook’s team. A novel addition to the second edition is a comprehensive collection of multimedia references to more than 700 videos, which bring valuable insight into the contents. The videos can be viewed directly augmented into the text with a smartphone or tablet using a unique and specially designed app. Springer Handbook of Robotics Multimedia Extension Portal: http://handbookofrobotics.org/
Basic Human-robot Interaction
Author: David O JohnsonPublisher: World Scientific
ISBN: 9811282862
Category : Technology & Engineering
Languages : en
Pages : 325
Book Description
The book's content is designed to provide practical guidance and insights for conducting experiments in Human-Robot Interaction (HRI) and publishing the results in scientific journals. It includes a detailed explanation of how to conduct HRI experiments and what to do and what not to do to get an article accepted for publication. It is tailored to those seeking to deepen their understanding of HRI methodologies, statistical measurements, and research design. The case studies and examples featured in the book focus on interactions between social robots and specific demographics such as children and older adults, making it relevant for individuals working in healthcare, education, and related domains.Also covered are common statistical measurements used in HRI research and quantitative, qualitative, and meta-analyses. The concepts are illustrated with several international case studies of interactions between social robots and children and older adults and robot learning instead of programming. The final chapter explores current trends in HRI and provides insights into what to look for in the coming years. It includes an extensive reference section to help HRI researchers in all these areas.This book will appeal to an international audience of advanced students, researchers, industry, and others who are actively engaged or interested in the field of HRI.
Recent Advances in Robot Learning
Author: Judy A. FranklinPublisher: Springer Science & Business Media
ISBN: 9780792397458
Category : Computers
Languages : en
Pages : 226
Book Description
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).
Learning for Adaptive and Reactive Robot Control
Author: Aude BillardPublisher: MIT Press
ISBN: 0262367017
Category : Technology & Engineering
Languages : en
Pages : 425
Book Description
Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises. This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills. Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control . Features for teaching in each chapter: applications, which range from arm manipulators to whole-body control of humanoid robots; pencil-and-paper and programming exercises; lecture videos, slides, and MATLAB code examples available on the author’s website . an eTextbook platform website offering protected material[EPS2] for instructors including solutions.
Designing Robot Behavior in Human-Robot Interactions
Author: Changliu LiuPublisher: CRC Press
ISBN: 0429602855
Category : Computers
Languages : en
Pages : 206
Book Description
In this book, we have set up a unified analytical framework for various human-robot systems, which involve peer-peer interactions (either space-sharing or time-sharing) or hierarchical interactions. A methodology in designing the robot behavior through control, planning, decision and learning is proposed. In particular, the following topics are discussed in-depth: safety during human-robot interactions, efficiency in real-time robot motion planning, imitation of human behaviors from demonstration, dexterity of robots to adapt to different environments and tasks, cooperation among robots and humans with conflict resolution. These methods are applied in various scenarios, such as human-robot collaborative assembly, robot skill learning from human demonstration, interaction between autonomous and human-driven vehicles, etc. Key Features: Proposes a unified framework to model and analyze human-robot interactions under different modes of interactions. Systematically discusses the control, decision and learning algorithms to enable robots to interact safely with humans in a variety of applications. Presents numerous experimental studies with both industrial collaborative robot arms and autonomous vehicles.
Robot Learning Human Skills and Intelligent Control Design
Author: Chenguang YangPublisher: CRC Press
ISBN: 1000395170
Category : Computers
Languages : en
Pages : 184
Book Description
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.