"This textbook offers an overview of techniques stemming from machine learning to train robots to adapt to changes in their environment"--Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Aude 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.
Inhaltsangabe
Preface xiii Notation xix I Introduction 1 1 Using and Learning Dynamical Systems for Robot Control--Overview 3 2 Gathering Data for Learning 27 II Learning a Controller 43 3 Learning a Control Law 45 4 Learning Multiple Control Laws 111 5 Learning Sequences of Control Laws 131 III Coupling and Modulating Controllers 173 6 Coupling and Synchronizing Controllers 175 7 Reaching for and Adapting to Moving Objects 195 8 Adapting and Modulating an Existing Control Law 219 9 Obstacle Avoidance 245 IV Compliant and Force Control with Dynamical Systems 267 10 Compliant Control 269 11 Force Control 295 12 Conclusion and Outlook 303 V Appendices A Background on Dynamical Systems Theory 307 B Background on Machine Learning 315 C Background on Robot Control 357 D Proofs and Derivations 361 Notes 379 Bibliography 383 Index 391
Preface xiii Notation xix I Introduction 1 1 Using and Learning Dynamical Systems for Robot Control--Overview 3 2 Gathering Data for Learning 27 II Learning a Controller 43 3 Learning a Control Law 45 4 Learning Multiple Control Laws 111 5 Learning Sequences of Control Laws 131 III Coupling and Modulating Controllers 173 6 Coupling and Synchronizing Controllers 175 7 Reaching for and Adapting to Moving Objects 195 8 Adapting and Modulating an Existing Control Law 219 9 Obstacle Avoidance 245 IV Compliant and Force Control with Dynamical Systems 267 10 Compliant Control 269 11 Force Control 295 12 Conclusion and Outlook 303 V Appendices A Background on Dynamical Systems Theory 307 B Background on Machine Learning 315 C Background on Robot Control 357 D Proofs and Derivations 361 Notes 379 Bibliography 383 Index 391
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