Reinforcement Learning Inspired Disturbance Rejection and Nao Bipedal Locomotion

Competitive bipedal soccer playing robots need to move fast and react quickly to changes in direction while staying upright. This paper describes the application of reinforcement learning to stabilise a flat-footed humanoid robot. An optimal control policy is learned using a physics simulator. The learned policy is supported theoretically and interpreted on a real robot as a linearised continuous control function. The paper also describes other components, including foot-step coordination, of bipedal locomotion integrated to achieve reactive omni-directional locomotion for Nao robots used in the RoboCup Standard Platform League.