Passive Biped Gait Stabilization: From Proportional Control to Deep Reinforcement Learning

This paper investigates the stabilization of passive bipedal robots using a Soft Actor-Critic (SAC) reinforcement learning approach. The primary objective is to enhance the robot's dynamic stability while navigating various slopes, particularly focusing on achieving a periodic solution. Our findings reveal that the robot can maintain stable walking up to a slope of 0.0252 radians. The SAC agent enables direct control of the ankle angle, eliminating the need for prior identification of unstable limit cycle conditions and sensitivity analysis for control gains. This research highlights the potential of reinforcement learning in improving robotic locomotion. It presents a framework for future investigations into adaptive control strategies that can effectively manage the complexities of bipedal passive motion.