让机器人自然跑跳，通过分解动作与修正实现平滑变速
RuN: Residual Policy for Natural Humanoid Locomotion
- 用预训练动作生成器提供自然动作先验，再由强化学习学残差修正
- 0-2.5米/秒速度范围稳定行走跑步切换，性能优于现有方法
- 适合研究人形机器人运动控制或想快速部署动态步态的开发者
使人形机器人在多种速度下实现自然、动态的运动，包括从步行到跑步的平滑过渡，仍是重大挑战。现有深度强化学习方法通常要求策略直接追踪参考运动，迫使单一策略同时学习动作模仿、速度跟踪和稳定性维持。为此，我们提出RuN，一种新型解耦残差学习框架。RuN通过将预训练的条件动作生成器（提供运动学自然的动作先验）与强化学习策略结合，后者学习轻量级残差修正以应对动力学交互。在Unitree G1人形机器人上的仿真与真实实验表明，RuN在0-2.5米/秒的速度范围内实现了稳定、自然的步态和顺畅的走跑转换，且在训练效率和最终性能上均优于当前最优方法。
原文摘要 · Abstract (English)
Enabling humanoid robots to achieve natural and dynamic locomotion across a wide range of speeds, including smooth transitions from walking to running, presents a significant challenge. Existing deep reinforcement learning methods typically require the policy to directly track a reference motion, forcing a single policy to simultaneously learn motion imitation, velocity tracking, and stability maintenance. To address this, we introduce RuN, a novel decoupled residual learning framework. RuN decomposes the control task by pairing a pre-trained Conditional Motion Generator, which provides a kinematically natural motion prior, with a reinforcement learning policy that learns a lightweight residual correction to handle dynamical interactions. Experiments in simulation and reality on the Unitree G1 humanoid robot demonstrate that RuN achieves stable, natural gaits and smooth walk-run transitions across a broad velocity range (0-2.5 m/s), outperforming state-of-the-art methods in both training efficiency and final performance.
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