A Surrogate Model Based Gait Learning for Biped Robot

Abstract

Gait learning is usually under a so-called simulation based framework, where a simulation platform is firstly setup, and then based on which the gait pattern is learned via some learning algorithm. For the reason that there exist big differences between simulation platform and real circumstances, an additional adapting procedure is always required when learned gait pattern is applied to a real robot. This case turns out to be more critical for a biped robot, because its control appears more difficult than others, such as a quadruped robot. This leads the new scheme that the gait is directly learned on real robot to be attractive. However, under this real robot based learning scheme, most of those learning algorithms that commonly used under simulation based framework appear to be trivial, since they always needs too many learning trials which may wear out the robot hardware. Faced to this situation, in this paper, a surrogate model based gait learning approach for biped robot is proposed. And the experimental results on a real humanoid robot PKU-HR3 show the effectiveness of the proposed approach.