{"product_id":"human-robot-interaction-control-using-reinforcement-learning-isbn-9781119782742","title":"Human-Robot Interaction Control Using Reinforcement Learning","description":"\u003cp\u003e\u003cb\u003eA comprehensive exploration of the control schemes of human-robot interactions \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn \u003ci\u003eHuman-Robot Interaction Control Using Reinforcement Learning\u003c\/i\u003e, 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. \u003c\/p\u003e \u003cp\u003e\u003ci\u003eHuman-Robot Interaction Control Using Reinforcement Learning \u003c\/i\u003eoffers 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. \u003c\/p\u003e \u003cp\u003eThe 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. \u003c\/p\u003e \u003cp\u003eReaders will also enjoy:  \u003c\/p\u003e \u003cul\u003e \u003cli\u003eA thorough introduction to model-based human-robot interaction control \u003c\/li\u003e \u003cli\u003eComprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles \u003c\/li\u003e \u003cli\u003ePractical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control \u003c\/li\u003e \u003cli\u003eIn-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning  \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect 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, \u003ci\u003eHuman-Robot Interaction Control Using Reinforcement Learning\u003c\/i\u003e is also an indispensable resource for students and professionals studying reinforcement learning. \u003c\/p\u003e \u003cp\u003eAuthor Biographies xi\u003c\/p\u003e \u003cp\u003eList of Figures xiii\u003c\/p\u003e \u003cp\u003eList of Tables xvii\u003c\/p\u003e \u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Human-robot Interaction Control 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Human-Robot Interaction Control 3\u003c\/p\u003e \u003cp\u003e1.2 Reinforcement Learning for Control 6\u003c\/p\u003e \u003cp\u003e1.3 Structure of the Book 7\u003c\/p\u003e \u003cp\u003eReferences 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Environment Model of Human-Robot Interaction 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Impedance and Admittance 17\u003c\/p\u003e \u003cp\u003e2.2 Impedance Model for Human-Robot Interaction 21\u003c\/p\u003e \u003cp\u003e2.3 Identification of Human-Robot Interaction Model 24\u003c\/p\u003e \u003cp\u003e2.4 Conclusions 30\u003c\/p\u003e \u003cp\u003eReferences 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Model Based Human-Robot Interaction Control 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Task Space Impedance\/Admittance Control 33\u003c\/p\u003e \u003cp\u003e3.2 Joint Space Impedance Control 36\u003c\/p\u003e \u003cp\u003e3.3 Accuracy and Robustness 37\u003c\/p\u003e \u003cp\u003e3.4 Simulations 39\u003c\/p\u003e \u003cp\u003e3.5 Conclusions 42\u003c\/p\u003e \u003cp\u003eReferences 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Model Free Human-Robot Interaction Control 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Task-Space Control Using Joint-Space Dynamics 45\u003c\/p\u003e \u003cp\u003e4.2 Task-Space Control Using Task-Space Dynamics 52\u003c\/p\u003e \u003cp\u003e4.3 Joint Space Control 53\u003c\/p\u003e \u003cp\u003e4.4 Simulations 54\u003c\/p\u003e \u003cp\u003e4.5 Experiments 55\u003c\/p\u003e \u003cp\u003e4.6 Conclusions 68\u003c\/p\u003e \u003cp\u003eReferences 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Human-in-the-loop Control Using Euler Angles 73\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 73\u003c\/p\u003e \u003cp\u003e5.2 Joint-Space Control 74\u003c\/p\u003e \u003cp\u003e5.3 Task-Space Control 79\u003c\/p\u003e \u003cp\u003e5.4 Experiments 83\u003c\/p\u003e \u003cp\u003e5.5 Conclusions 92\u003c\/p\u003e \u003cp\u003eReferences 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Reinforcement Learning for Robot Interaction Control 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Reinforcement Learning for Robot Position\/Force Control 99\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 99\u003c\/p\u003e \u003cp\u003e6.2 Position\/Force Control Using an Impedance Model 100\u003c\/p\u003e \u003cp\u003e6.3 Reinforcement Learning Based Position\/Force Control 103\u003c\/p\u003e \u003cp\u003e6.4 Simulations and Experiments 110\u003c\/p\u003e \u003cp\u003e6.5 Conclusions 117\u003c\/p\u003e \u003cp\u003eReferences 117\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Continuous-Time Reinforcement Learning for Force Control 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 119\u003c\/p\u003e \u003cp\u003e7.2 K-means Clustering for Reinforcement Learning 120\u003c\/p\u003e \u003cp\u003e7.3 Position\/Force Control Using Reinforcement Learning 124\u003c\/p\u003e \u003cp\u003e7.4 Experiments 130\u003c\/p\u003e \u003cp\u003e7.5 Conclusions 136\u003c\/p\u003e \u003cp\u003eReferences 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Robot Control in Worst-Case Uncertainty Using Reinforcement Learning 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 139\u003c\/p\u003e \u003cp\u003e8.2 Robust Control Using Discrete-Time Reinforcement Learning 141\u003c\/p\u003e \u003cp\u003e8.3 Double Q-Learning with k-Nearest Neighbors 144\u003c\/p\u003e \u003cp\u003e8.4 Robust Control Using Continuous-Time Reinforcement Learning 150\u003c\/p\u003e \u003cp\u003e8.5 Simulations and Experiments: Discrete-Time Case 154\u003c\/p\u003e \u003cp\u003e8.6 Simulations and Experiments: Continuous-Time Case 161\u003c\/p\u003e \u003cp\u003e8.7 Conclusions 170\u003c\/p\u003e \u003cp\u003eReferences 170\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Redundant Robots Control Using Multi-Agent Reinforcement Learning 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 173\u003c\/p\u003e \u003cp\u003e9.2 Redundant Robot Control 175\u003c\/p\u003e \u003cp\u003e9.3 Multi-Agent Reinforcement Learning for Redundant Robot Control 179\u003c\/p\u003e \u003cp\u003e9.4 Simulations and experiments 183\u003c\/p\u003e \u003cp\u003e9.5 Conclusions 187\u003c\/p\u003e \u003cp\u003eReferences 189\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Robot \u003ci\u003eH\u003c\/i\u003e\u003csub\u003e2\u003c\/sub\u003e Neural Control Using Reinforcement Learning 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 193\u003c\/p\u003e \u003cp\u003e10.2 \u003ci\u003eH\u003c\/i\u003e\u003csub\u003e2\u003c\/sub\u003e Neural Control Using Discrete-Time Reinforcement Learning 194\u003c\/p\u003e \u003cp\u003e10.3 \u003ci\u003eH\u003c\/i\u003e\u003csub\u003e2\u003c\/sub\u003e Neural Control in Continuous Time 207\u003c\/p\u003e \u003cp\u003e10.4 Examples 219\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 229\u003c\/p\u003e \u003cp\u003eReferences 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Conclusions 233\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA Robot Kinematics and Dynamics 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Kinematics 235\u003c\/p\u003e \u003cp\u003eA.2 Dynamics 237\u003c\/p\u003e \u003cp\u003eA.3 Examples 240\u003c\/p\u003e \u003cp\u003eReferences 246\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Reinforcement Learning for Control 247\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Markov decision processes 247\u003c\/p\u003e \u003cp\u003eB.2 Value functions 248\u003c\/p\u003e \u003cp\u003eB.3 Iterations 250\u003c\/p\u003e \u003cp\u003eB.4 TD learning 251\u003c\/p\u003e \u003cp\u003eReference 258\u003c\/p\u003e \u003cp\u003eIndex 259\u003c\/p\u003e \u003cp\u003e\u003cb\u003eWEN YU, PhD,\u003c\/b\u003e 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 \u003ci\u003eModeling and Control of Uncertain Nonlinear Systems with Fuzzy Equations and Z-Number.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eADOLFO PERRUSQUÍA, PhD,\u003c\/b\u003e is a Research Fellow in the School of Aerospace, Transport, and Manufacturing at Cranfield University in Bedford, UK.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA comprehensive exploration of the control schemes of human-robot interactions\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn \u003ci\u003eHuman-Robot Interaction Control Using Reinforcement Learning\u003c\/i\u003e, 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eHuman-Robot Interaction Control Using Reinforcement Learning\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003eReaders will also enjoy: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA thorough introduction to model-based human-robot interaction control\u003c\/li\u003e \u003cli\u003eComprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles\u003c\/li\u003e \u003cli\u003ePractical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control\u003c\/li\u003e \u003cli\u003eIn-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect 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, \u003ci\u003eHuman-Robot Interaction Control Using Reinforcement Learning\u003c\/i\u003e is also an indispensable resource for students and professionals studying reinforcement learning.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989390803173,"sku":"NP9781119782742","price":150.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119782742.jpg?v=1761783925","url":"https:\/\/k12savings.com\/es\/products\/human-robot-interaction-control-using-reinforcement-learning-isbn-9781119782742","provider":"K12savings","version":"1.0","type":"link"}