Human-Robot Interaction Control Using Reinforcement Learning

Human-Robot Interaction Control Using Reinforcement Learning provides a comprehensive overview of control schemes and insightful presentations of novel, model-free, and reinforcement learning controllers. It offers rigorous mathematical treatments and demonstrations, discusses stability and convergence analysis, and covers advanced topics like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in robotics. It is a valuable resource for students and professionals studying and working in robotics, learning control systems, neural networks, and computational intelligence.

Format: Hardback
Length: 288 pages
Publication date: 05 November 2021
Publisher: John Wiley and Sons Ltd

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.

Weight: 534g
Dimension: 233 x 162 x 20 (mm)
ISBN-13: 9781119782742