用深度强化学习让人形机器人稳定跑出3.5米/秒高速
Achieving Stable High-Speed Locomotion for Humanoid Robots with Deep Reinforcement Learning
- 结合强化学习与运动动力学先验，协调四肢保持平衡
- 在仿真中实现3.5米/秒速度追踪，波动显著减少
- 适合需高速稳定行走的复杂场景应用
人形机器人虽具备广泛任务适应性，但在高速行走与奔跑方面仍面临挑战。本文提出一种融合深度强化学习与运动动力学先验的稳定步态控制方法（KSLC），通过协调手臂运动抵消不稳定力，提升整体稳定性。相比基线方法，KSLC在速度追踪精度和泛化能力上表现更优。仿真测试中，该方法使机器人成功追踪3.5米/秒的目标速度，波动明显降低。在高保真仿真环境中的模拟-模拟验证进一步证明其鲁棒性，展现出实际应用潜力。
原文摘要 · Abstract (English)
Humanoid robots offer significant versatility for performing a wide range of tasks, yet their basic ability to walk and run, especially at high velocities, remains a challenge. This letter presents a novel method that combines deep reinforcement learning with kinodynamic priors to achieve stable locomotion control (KSLC). KSLC promotes coordinated arm movements to counteract destabilizing forces, enhancing overall stability. Compared to the baseline method, KSLC provides more accurate tracking of commanded velocities and better generalization in velocity control. In simulation tests, the KSLC-enabled humanoid robot successfully tracked a target velocity of 3.5 m/s with reduced fluctuations. Sim-to-sim validation in a high-fidelity environment further confirmed its robust performance, highlighting its potential for real-world applications.
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