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Human-Robot Interaction Control Using Reinforcement Learning

Book Hero Magic crafted this summary to help describe this book. While it's new and still learning, it may not be perfect - your feedback is welcome! Summary
Human-Robot Interaction Control Using Reinforcement Learning presents a detailed examination of control schemes in human-robot interaction, focusing on reinforcement learning techniques. The book covers state-of-the-art control methods, environment modelling, and parameter estimation, supplemented by rigorous mathematical treatments. Readers will explore stability and convergence analyses, inverse and velocity kinematics, H2 neural control, and advanced topics such as multi-agent reinforcement learning for redundant robot control. This comprehensive guide is ideal for those seeking a deep understanding of both model-based and model-free human-robot interaction control.
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Format: Hardback
$28799
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Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?

Ideal for senior undergraduate and graduate students, academic researchers, and industry professionals engaged in robotics, learning control systems, neural networks, and computational intelligence. Also highly valuable for anyone studying or working with reinforcement learning and advanced robot control techniques.

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Book Hero Magic formatted this description to make it easier to read. While it's new and still learning, it may not be perfect - your feedback is welcome! 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.

Series: IEEE Press Series on Systems Science and Engineering

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Book Details

INFORMATION

ISBN: 9781119782742

Publisher: John Wiley & Sons Inc

Format: Hardback

Date Published: 05 November 2021

Country: United States

Imprint: Wiley-IEEE Press

Audience: Professional and scholarly

DIMENSIONS

Spine width: 10.0mm

Width: 10.0mm

Height: 10.0mm

Weight: 454g

Pages: 288

About the Author

WEN YU, PhD, is Professor and Head of the Departamento de Control AutomΓ‘tico with the Centro de InvestigaciΓ³n y de Estudios Avanzados, Instituto PolitΓ©cnico Nacional (CINVESTAV-IPN), Mexico City, Mexico. He is a co-author of Modeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number.

ADOLFO PERRUSQUÍA, PhD, is a Research Fellow in the School of Aerospace, Transport, and Manufacturing at Cranfield University in Bedford, UK.

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