Human-robot interaction control using reinforcement learning

by Yu, Wen, (Robotics engineer)

Type Book

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Title Statements

Full title :Human-robot interaction control using reinforcement learning / Wen Yu, Adolfo Perrusquia.

Summary

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.

Edition Statements

Publication Date:2022

Publication Statements

Publisher(s):Wiley-IEEE Press
Place of Publication:Hoboken, New Jersey
Language:eng
Publication Date:2022

The Physical Object

Description:1 online resource

Edition Identifiers

ISBN:9781119782773, 1119782775, 9781119782759, 1119782759, 9781119782766, 1119782767
DOI:10.1002/9781119782773
oclc:on1273077343
LC Classification:TJ211.49
Dewey Decimal:629.8924019

Cover -- Title Page -- Copyright -- Contents -- Author Biographies -- List of Figures -- List of Tables -- Preface -- Part I Human-robot Interaction Control -- Chapter 1 Introduction -- 1.1 Human-Robot Interaction Control -- 1.2 Reinforcement Learning for Control -- 1.3 Structure of the Book -- References -- Chapter 2 Environment Model of Human-Robot Interaction -- 2.1 Impedance and Admittance -- 2.2 Impedance Model for Human-Robot Interaction -- 2.3 Identification of Human-Robot Interaction Model -- 2.4 Conclusions -- References -- Chapter 3 Model Based Human-Robot Interaction Control -- 3.1 Task Space Impedance/Admittance Control -- 3.2 Joint Space Impedance Control -- 3.3 Accuracy and Robustness -- 3.4 Simulations -- 3.5 Conclusions -- References -- Chapter 4 Model Free Human-Robot Interaction Control -- 4.1 Task-Space Control Using Joint-Space Dynamics -- 4.2 Task-Space Control Using Task-Space Dynamics -- 4.3 Joint Space Control -- 4.4 Simulations -- 4.5 Experiments -- 4.6 Conclusions -- References -- Chapter 5 Human-in-the-loop Control Using Euler Angles -- 5.1 Introduction -- 5.2 Joint-Space Control -- 5.3 Task-Space Control -- 5.4 Experiments -- 5.5 Conclusions -- Referenc…

Subjects

Apprentissage par renforcement (Intelli… Commande intelligente Electronic books human robot interaction Intelligent control systems Interaction homme-robot Reinforcement learning