{"product_id":"learning-for-adaptive-and-reactive-robot-control-isbn-9780262046169","title":"Learning for Adaptive and Reactive Robot Control","description":"\u003cb\u003eMethods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eThis book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills. \u003cbr\u003e \u003cbr\u003e\u003cbr\u003e\u003ci\u003eLearning for Adaptive and Reactive Robot Control\u003c\/i\u003e is designed for graduate-level courses in robotics,  with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control . \u003cbr\u003eFeatures for teaching in each chapter: \u003cbr\u003e \u003cbr\u003e\u003cli\u003eapplications, which range from arm manipulators to whole-body control of humanoid robots;\u003c\/li\u003e\u003cli\u003epencil-and-paper and programming exercises;\u003c\/li\u003e\u003cli\u003electure videos, slides, and MATLAB code examples available on the author’s website . \u003c\/li\u003e\u003cli\u003ean eTextbook platform website offering protected material[EPS2]  for instructors including solutions.\u003c\/li\u003e \u003cbr\u003e Preface xiii\u003cbr\u003eNotation xix\u003cbr\u003eI Introduction 1\u003cbr\u003e1 Using and Learning Dynamical Systems for Robot Control--Overview 3\u003cbr\u003e2 Gathering Data for Learning 27\u003cbr\u003eII Learning a Controller 43\u003cbr\u003e3 Learning a Control Law 45\u003cbr\u003e4 Learning Multiple Control Laws 111\u003cbr\u003e5 Learning Sequences of Control Laws 131\u003cbr\u003eIII Coupling and Modulating Controllers 173\u003cbr\u003e6 Coupling and Synchronizing Controllers 175\u003cbr\u003e7 Reaching for and Adapting to Moving Objects 195\u003cbr\u003e8 Adapting and Modulating an Existing Control Law 219\u003cbr\u003e9 Obstacle Avoidance 245\u003cbr\u003eIV Compliant and Force Control with Dynamical Systems 267\u003cbr\u003e10 Compliant Control 269\u003cbr\u003e11 Force Control 295\u003cbr\u003e12 Conclusion and Outlook 303\u003cbr\u003eV Appendices \u003cbr\u003eA Background on Dynamical Systems Theory 307\u003cbr\u003eB Background on Machine Learning 315\u003cbr\u003eC Background on Robot Control 357\u003cbr\u003eD Proofs and Derivations 361\u003cbr\u003eNotes 379\u003cbr\u003eBibliography 383\u003cbr\u003eIndex 391Aude Billard is Professor, School of Engineering, Ecole Polytechnique Federale de Lausanne (EPFL) and Director of the Learning Algorithms and Systems Laboratory (LASA). Sina Mirrazavi is a Senior Researcher at Sony. Nadia Figueroa is the Shalini and Rajeev Misra Presidential Assistant Professor in the Mechanical Engineering and Applied Mechanics (MEAM) Department at the University of Pennsylvania.","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":46301026353381,"sku":"NP9780262046169","price":85.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262046169.jpg?v=1767731239","url":"https:\/\/k12savings.com\/products\/learning-for-adaptive-and-reactive-robot-control-isbn-9780262046169","provider":"K12savings","version":"1.0","type":"link"}