Iterative learning control for high-fidelity tracking of fast motions on entertainment humanoid robots

Abstract

Creating animations for entertainment humanoid robots is a time consuming process because of high aesthetic quality requirements as well as poor tracking performance due to small actuators used in order to realize human size. Once deployed, such robots are also expected to work for years with minimum downtime for maintenance. In this paper, we demonstrate a successful application of an iterative learning control algorithm to automate the process of fine tuning choreographed human-speed motions on a 37 degree-of-freedom humanoid robot. By using good initial feed-forward commands generated by experimentally-identified joint models, the learning algorithm converges in about 9 iterations and achieves almost the same fidelity as the manually fine tuned motion.