Article

Self-Balancing Reinforcement Learning for Wheeled-Legged Robots Based on MuJoCo and PPO

Starting from the instability mechanism of a rolling inverted pendulum, the article explains why two-wheeled-legged robots must rely on wheel rolling to maintain balance, and presents the complete mathematical formulation of proximal policy optimization (PPO). Using a real training experiment of three million steps, the author shows the policy's progression from random jitter to standing in place and moving toward a target.

Part of the event: Explaining reinforcement learning training methods for self-balancing wheeled-legged robots

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