Online Learning Upper Body Posture Control for Humanoid Walking Based on BPNN

Bipedal walking is the basis for the humanoid robot to execute all the work. This paper proposed an online learning upper body posture control strategy for humanoid walking. The proposed posture controller is based on real-time adjustment of the hip joint, and keeps upper body posture upright stabilizing the robot. A backpropagation neural network (BPNN) controller is trained using the dataset from different PID controllers. A policy gradient reinforcement learning method is proposed to adjust the network parameters online according to the sensor feedback in different environments. Experiments on a NAO humanoid robot show the effectiveness of the proposed methods.