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To improve the robustness of biped walking, a model parameters optimization method based on policy gradient decent learning is presented. For the linear inverted pendulum mode-based model parameters optimization, firstly, select the input parameters of the inverted pendulum model and the torso attitude parameters of the robot as the correction variables and establish the correction equation. Then, using the tracking errors of center of mass (CoM) of the robot and the errors of the robot posture relative to the upright state of the body to establish the fitness function. According to the fitness function, the gain coefficients in the model parameters correction equation are optimized by using the strategy gradient learning method, and the modified gain parameters are substituted into the model parameters correction equation to obtain the correction amount. By applying the model parameters optimization strategy, the robot can quickly and in real time adjust the body posture and walking patterns under unknown disturbances, hence, the walking robustness can be enhanced. Simulation and experiments on a full-body humanoid robot NAO validate the effectiveness of the proposed method. The experiments show that the optimized model yields a more controlled, robust walk on NAO robot and on various surfaces without additional manual parameters tuning.

Dynamic walking control of humanoid robots combining linear inverted pendulum mode with parameter optimization