Learning Dextrous Manipulation Skills for Multifingered Robot Hands

Olac Fuentes and Randal C. Nelson
Department of Computer Science
University of Rochester
Rochester, NY 14627

Abstract: We present a method for autonomous learning of dextrous manipulation skills with robot hands. We use heuristics derived from observations made on human hands to reduce the degrees of freedom of the task and make learning tractable. Our approach consists of learning and storing a few manipulation primitives for a few prototypical objects and then using an associative memory to obtain the required parameters for new objects and/or manipulations. The parameter space of the robot is searched using a modified version of the evolution strategy. Our system does not rely on simulation; all the experimentation is performed by a physical robot, the 16-degree-of-freedom Utah/MIT hand. Experimental results show that accurate dextrous manipulation skills can be learned in a short period of time.