Abstract: A foot positioning compensator is developed in this paper for a full-body humanoid to retrieve its balance during continuous walking.
An online Foot Position Compensator (FPC) is designed to improve the robustness of biped walking, which can modify predefined step
position and step duration online with sensory feedback. Foot placement parameters are learned by the FPC based on the Policy Gradient
Reinforcement Learning (PGRL) method. Moreover, the FPC assists the humanoid robot in rejecting external disturbances and recovering
the walking position by re-planning the trajectories of walking pattern and the Center of Mass (CoM). An upper body pose control strategy
is also presented to further enhance the performance of humanoid robots to overcome strong external disturbances. The advantages of this proposed method are that it neither requires prior information about the walking terrain conditions, nor relies on range sensor information for surface topology measurement. The effectiveness of the proposed method is verified via Webots simulation and real experiments on a full-body humanoid NAO robot.

Key words: humanoid walking, active balance, Foot Positioning Compensation (FPC), Policy Gradient Reinforcement Learning (PGRL)