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Human-Robot Interaction Control Using Reinforcement Learning 1st edition Yu

  • SKU: BELL-34951208
Human-Robot Interaction Control Using Reinforcement Learning 1st edition Yu
$ 31.00 $ 45.00 (-31%)

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Human-Robot Interaction Control Using Reinforcement Learning 1st edition Yu instant download after payment.

Publisher: Wiley-IEEE Press
File Extension: PDF
File size: 25.5 MB
Pages: 288
Author: Yu, Wen, Perrusquia, Adolfo
ISBN: 9781119782742, 1119782740
Language: English
Year: 2021
Edition: 1

Product desciption

Human-Robot Interaction Control Using Reinforcement Learning 1st edition Yu by Yu, Wen, Perrusquia, Adolfo 9781119782742, 1119782740 instant download after payment.

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. 

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