Reinforcement learning method-based stable gait synthesis for biped robot

Abstract

A stable gait generation algorithm based on T-S type fuzzy learning net is proposed in this paper. Gait generation is divided into model construction and error learning. Reference gait model and dynamic model are firstly constructed with basic gait geometric knowledge. Then reinforcement learning method is introduced into T-S type fuzzy network to learn the gain parameters for hip trajectory adjustment. Few fuzzy rules with ZMP stable knowledge are needed to formulate the nonlinear relation between the ZMP curve and hip trajectory. The problem of finding multi-variables in continuous space is also simplified to searching independent action gains simultaneously. Results of simulation on a biped robot proved the feasibility.