Learning CPG sensory feedback with policy gradient for biped locomotion for a full-body humanoid

This paper describes a learning framework for a central pattern generator based biped locomotion controller using a policy gradient method. Our goals in this study are to achieve biped walking with a 3D hardware humanoid, and to develop an efficient learning algorithm with CPG by reducing the dimensionality of the state space used for learning. We demonstrate that an appropriate feed-back controller can be acquired within a thousand trials by numerical simulations and the obtained controller in numerical simulation achieves stable walking with a physical robot in the real world. Numerical simulations and hardware experiments evaluated walking velocity and stability. Furthermore, we present the possibility of an additional online learning using a hardware robot to improve the controller within 200 iterations.