Towards a theory of robot motor control

Abstract

We propose some basic elements of a robot motor control system that have direct counterparts in human motor control, based on the premise that optimality is the fundamental principle underlying both human and robot motor control. We first review some of the basic principles and hypotheses from human motor control, particularly those mechanisms for coping with the degrees of freedom problem, and the role of noise, feedback, and attention in motor control and learning. The state-of-the-art in robot motion optimization and optimal control is then examined, focusing on robust algorithms for generating optimal trajectories, and the use of dimension reduction techniques from machine learning. We then propose a new class of problems that are a direct consequence of some of the optimality paradigms from human motor control — these include kinematic feedback control laws for generating natural motions based on the minimum variance principle, and an LQR tracking control laws that minimize attention — and examine how their solutions can be used as primitives in a robot motor control system.