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arXiv · 2509.20696

RuN: Residual Policy for Natural Humanoid Locomotion

Qingpeng Li·Chengrui Zhu·Yanming Wu·Xin Yuan·Zhen Zhang·Jian Yang·Yong Liu
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Abstract

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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Qingpeng Li, Chengrui Zhu, Yanming Wu, Xin Yuan, Zhen Zhang, Jian Yang, Yong Liu. 2025-09-25. RuN: Residual Policy for Natural Humanoid Locomotion. https://arxiv.org/abs/2509.20696

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