Kicking motion design of humanoid robots using gradual accumulation learning method based on Q-learning

Abstract

This paper manly presented kicking design motion of humanoid robots using a reinforcement learning method which is based on the Q-learning. First, this method build a multidirectional fixed-point kicking model, which is based on the offset of kicking point, the foot space motion trajectory and ZMP stability criterion, and that makes subsequent train costs much less time. Besides, discretization of state set is also used to improve the training method. Compared to other machine learning algorithms, this method reduces the dimension of the system and solves the problem of excessive train when kicking in long distance. A series of experiments proves that the method described in this paper is feasible and effective.