<p><b>A comprehensive exploration of the control schemes of human-robot interactions </b></p> <p>In <i>Human-Robot Interaction Control Using Reinforcement Learning</i>, 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. </p> <p><i>Human-Robot Interaction Control Using Reinforcement Learning </i>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. </p> <p>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. </p> <p>Readers will also enjoy: </p> <ul> <li>A thorough introduction to model-based human-robot interaction control </li> <li>Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles </li> <li>Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control </li> <li>In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning </li> </ul> <p>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, <i>Human-Robot Interaction Control Using Reinforcement Learning</i> is also an indispensable resource for students and professionals studying reinforcement learning. </p>
