Arm pose copying for humanoid robots

Abstract

Learning by imitation is becoming increasingly important for teaching humanoid robots new skills. The simplest form of imitation is behavior copying in which the robot is minimizing the difference between its perceived motion and that of the imitated agent. One problem that must be solved even in this simplest of all imitation tasks is calculating the learner's pose corresponding to the perceived pose of the agent it is imitating. This paper presents a general framework for solving this problem in closed form for the arms of a generalized humanoid robot of which most available humanoids are special cases. The paper also reports the evaluation of the proposed system for real and simulated robots.