Learning humanoid motion dynamics through sensory-motor mapping in reduced dimensional spaces

Optimization of robot dynamics for a given human motion is an intuitive way to approach the problem of learning complex human behavior by imitation. In this paper, we propose a methodology based on a learning approach that performs optimization of humanoid dynamics in a low-dimensional subspace. We compactly represent the kinematic information of humanoid motion in a low dimensional subspace. Motor commands in the low dimensional subspace are mapped to the expected sensory feedback. We select optimal motor commands based on sensory-motor mapping that also satisfy our kinematic constraints. Finally, we obtain a set of novel postures that result in superior motion dynamics compared to the initial motion. We demonstrate results of the optimized motion on both a dynamics simulator and a real humanoid robot