Methods by which robots can learn control laws that enable real-time
reactivity using dynamical systems; with applications and exercises.
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 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.
Learning for Adaptive and Reactive Robot Control 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 .
Features for teaching in each chapter:
applications, which range from arm manipulators to whole-body control of
humanoid robots;
pencil-and-paper and programming exercises;
lecture videos, slides, and MATLAB code examples available on the
author's website .
an eTextbook platform website offering protected material[EPS2] for
instructors including solutions.
