Human-Robot Interaction Control Using Reinforcement Learning (IEEE Press Series on Systems Science and Engineering) - Tapa dura

Acerca del autor

WEN YU, PhD, is Professor and Head of the Departamento de Control Automático with the Centro de Investigación y de Estudios Avanzados, Instituto Politécnico 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.

De la contraportada

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.