Crawling Posture Learning in Humanoid Robots using a Natural-Actor-Critic CPG Architecture

摘要

In this article, a four-cell CPG network, exploiting sensory feedback, is proposed in order to emulate infant crawling gaits when utilized on the NAO robot. Based on the crawling model, the positive episodic natural-actor-critic architecture is applied to learn a proper posture of crawling on a simu-lated NAO. By transferring the learned results to the physical NAO, the transferability from simulation to physical world is discussed. Finally, a discussion pertaining to locomotion learning based on dynamic system theory is given in the con-clusion.