Push Recovery Control for a Biped Robot Using DDPG Reinforcement Learning algorithm

A novel push recovery control for biped robots based on the LIP model using reinforcement learning (RL) is proposed. This research delves into the application of machine learning to enhance the control and adaptability of biped robots in uncertain environments Recovering the balance of standing against external disturbances is the main goal of this paper. Thus, we propose Deep Deterministic Policy Gradient (DDPG) as a powerful alternative instead of traditional methods for push recovery control. We use the SimScape toolbox of MATLAB to develop a 7-link multi-body model and train it via DDPG algorithm to recover its balance versus a range of push intensities disturb it. We evaluate success of this control strategy via simulations. The results show a significant improvement in balance recovery capabilities of our method.