Aude Billard, Sina Mirrazavi, Nadia Figueroa
The book will appear in the spring 2022. It can be purchased from MIT Press here.
This 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 re-plan 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.
The book is meant to be used as Textbook for graduate-level courses in robotics, and, hence, the chapters proceed from fundamentals to more advanced content. The first section presents an overview of the techniques introduced, including learning from demonstration, optimization, and reinforcement learning. Subsequent sections present the core techniques for learning control laws with dynamical systems, trajectory planning with dynamical systems, and methods for compliant and force control using dynamical systems.
Each chapter describes applications, which range from arm manipulators to whole-body control of humanoid robots, and offers both pencil-and-paper and programming exercises. Lecture videos, slides, and MATLAB code examples are available below.
Matlab code in support of programming exercises are posted on the book’s git-hub page HERE.
Material for Lecturers
Slides for lectures and video of lectures will be posted here in spring 2022. Stay tuned
Solutions to pen and paper exercises offered in the book can be obtained by lecturers upon request. These are
given only to lecturers who have purchased the book and if these solutions are to be used in a course. Send Email.