Human-robot interaction control using reinforcement learning

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Title:

Author:

Yu, Wen (Robotics engineer), author.

ISBN:

9781119782773

9781119782759

9781119782766

Physical Description:

1 online resource

Series:

IEEE Press series on systems science and engineering

IEEE Press series on systems science and engineering.

Contents:

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 -- References -- Part II Reinforcement Learning for Robot Interaction Control -- Chapter 6 Reinforcement Learning for Robot Position/Force Control -- 6.1 Introduction -- 6.2 Position/Force Control Using an Impedance Model -- 6.3 Reinforcement Learning Based Position/Force Control -- 6.4 Simulations and Experiments -- 6.5 Conclusions -- References -- Chapter 7 Continuous-Time Reinforcement Learning for Force Control -- 7.1 Introduction -- 7.2 K-means Clustering for Reinforcement Learning -- 7.3 Position/Force Control Using Reinforcement Learning -- 7.4 Experiments -- 7.5 Conclusions -- References -- Chapter 8 Robot Control in Worst-Case Uncertainty Using Reinforcement Learning.

8.1 Introduction -- 8.2 Robust Control Using Discrete-Time Reinforcement Learning -- 8.3 Double Q-Learning with k-Nearest Neighbors -- 8.4 Robust Control Using Continuous-Time Reinforcement Learning -- 8.5 Simulations and Experiments: Discrete-Time Case -- 8.6 Simulations and Experiments: Continuous-Time Case -- 8.7 Conclusions -- References -- Chapter 9 Redundant Robots Control Using Multi-Agent Reinforcement Learning -- 9.1 Introduction -- 9.2 Redundant Robot Control -- 9.3 Multi-Agent Reinforcement Learning for Redundant Robot Control -- 9.4 Simulations and experiments -- 9.5 Conclusions -- References -- Chapter 10 Robot ℋ2 Neural Control Using Reinforcement Learning -- 10.1 Introduction -- 10.2 ℋ2 Neural Control Using Discrete-Time Reinforcement Learning -- 10.3 ℋ2 Neural Control in Continuous Time -- 10.4 Examples -- 10.5 Conclusion -- References -- Chapter 11 Conclusions -- A Robot Kinematics and Dynamics -- A.1 Kinematics -- A.2 Dynamics -- A.3 Examples -- References -- B Reinforcement Learning for Control -- B.1 Markov decision processes -- B.2 Value functions -- B.3 Iterations -- B.4 TD learning -- Reference -- Index -- EULA.

Abstract:

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.

Local Note:

John Wiley and Sons

Subject Term:

Human-robot interaction.

Reinforcement learning.

Intelligent control systems.

Interaction homme-robot.

Apprentissage par renforcement (Intelligence artificielle)

Commande intelligente.

Human-robot interaction

Intelligent control systems

Reinforcement learning