"This textbook offers an overview of techniques stemming from machine learning to train robots to adapt to changes in their environment"--

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.

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