Learning Optimized Human Motion via Phase Space Analysis

Abstract

This paper proposes a dynamic system based learning from demonstration approach to teach a robot activities of daily living. The approach takes inspiration from human movement literature to formulate trajectory learning as an optimal control problem. We assume a weighted combination of basis objective functions is the true objective function for a demonstrated motion. We derive basis objective functions analogous to those in human movement literature to optimize the robot's motion. This method aims to naturally adapt the learned motion in different situations. To validate our approach, we learn motions from two categories: 1) commonly prescribed therapeutic exercises and 2) tea making. We show the reproduction accuracy of our method and compare torque requirements to the dynamic motion primitive for each motion, with and without an added load.