000			02872 a2200265 4500
003			OSt
005			20250227174636.0
008			250227b xxu||||| |||| 00| 0 eng d
020			_a9781119782742
040			_cIIT Kanpur
041			_aeng
082			_a629.892 _bY9h
100			_aYu, Wen
245			_aHuman-robot interaction control using reinforcement learning _cWen Yu and Adolfo Perrusquia
260			_bJohn Wiley _c2022 _aHoboken
300			_axx, 262p
440			_aIEEE Press series on systems science and engineering
490			_a/ edited by MengChu Zhou
520			_a 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.
650			_aHuman-robot interaction
650			_aIntelligent control systems
650			_aReinforcement learning
700			_aPerrusquia, Adolfo
942			_cBK
999			_c567398 _d567398