用生成模型指导机器人行走，让动作更自然稳定。
Natural Humanoid Robot Locomotion with Generative Motion Prior
- 用生成模型预训练自然动作参考，提供细粒度监督
- 仿真与真实环境均实现更自然的行走表现
- 适合关注机器人运动自然性的研究者与开发者
自然逼真的行走仍是人形机器人融入人类社会的核心挑战。以往方法或忽视动作自然性，或依赖不稳定的风格奖励。本文提出生成式运动先验（Generative Motion Prior, GMP），为自然人形机器人行走任务提供细粒度运动级监督。首先通过全身运动重定向将自然人类动作迁移至机器人；随后离线训练一个基于条件变分自编码器的生成模型，预测未来自然参考动作。在策略训练中，该生成模型作为冻结的在线动作生成器，实时提供关节角度与关键点位置等轨迹级精确指导。实验表明，该方法显著提升训练稳定性与可解释性，在仿真与真实环境中均优于现有方法，实现更自然的运动表现。
原文摘要 · Abstract (English)
Natural and lifelike locomotion remains a fundamental challenge for humanoid robots to interact with human society. However, previous methods either neglect motion naturalness or rely on unstable and ambiguous style rewards. In this paper, we propose a novel Generative Motion Prior (GMP) that provides fine-grained motion-level supervision for the task of natural humanoid robot locomotion. To leverage natural human motions, we first employ whole-body motion retargeting to effectively transfer them to the robot. Subsequently, we train a generative model offline to predict future natural reference motions for the robot based on a conditional variational auto-encoder. During policy training, the generative motion prior serves as a frozen online motion generator, delivering precise and comprehensive supervision at the trajectory level, including joint angles and keypoint positions. The generative motion prior significantly enhances training stability and improves interpretability by offering detailed and dense guidance throughout the learning process. Experimental results in both simulation and real-world environments demonstrate that our method achieves superior motion naturalness compared to existing approaches. Project page can be found at https://sites.google.com/view/humanoid-gmp
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