Reinforcement Learning-based Impact Forces-Optimized Gait Control for Humanoid Locomotion: Toward Smooth and Quiet Walking
This paper proposes a reinforcement learning-based Impact Forces-Optimized Gait Control framework for achieving smooth and quiet humanoid locomotion. The proposed method integrates a quintic polynomial swing-foot trajectory designed to ensure zero vertical velocity and acceleration at foot-ground contact with a joint-jerk minimization reward. Impact forces are provided as privileged observations during training to improve robustness under an asymmetric information architecture. The effectiveness of the proposed method was verified through evaluations conducted in Isaac Lab and MuJoCo (Multi-Joint dynamics with Contact), demonstrating reduced impact forces across various locomotion modes. Additionally, velocity tracking evaluations confirm that the proposed method does not significantly degrade locomotion performance. Furthermore, its performance was validated on the G1 humanoid robot, where real-world walking tests confirmed a reduction in walking-induced acoustic noise.