Natural Humanoid Walk Using Reinforcement Learning

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Introduced an end-to-end neural network for humanoid locomotion trained with reinforcement learning (RL). The system utilizes a high-fidelity physics simulator with domain randomization and zero-shot sim-to-real transfer via kHz-rate torque feedback. The RL policy is optimized to mimic human walking reference trajectories, balancing gait style with velocity tracking, power consumption, and robustness to perturbations and terrain variations. This enables repeatable, human-like walking across the fleet of Figure 02 robots.