PHUMA：基于物理的人形机器人运动数据集

摘要

运动模仿是实现仿人机器人运动的一种前景广阔的方法，可使智能体获得类人行为。现有方法通常依赖AMASS等高质量运动捕捉数据集，但这些数据稀缺且昂贵，限制了方法的可扩展性与多样性。近期研究尝试通过转换大规模网络视频（如Humanoid-X）来扩大数据采集规模，但常引入漂浮、穿透、足部滑移等物理伪影，阻碍了稳定模仿。为此，我们提出PHUMA——基于物理约束的仿人运动数据集，该方案在利用大规模人类视频数据的同时，通过精细数据筛选与物理约束的重定向技术解决物理伪影问题。PHUMA通过强制关节限制、确保地面接触并消除足部滑移，生成兼具大规模与物理可靠性的运动数据。我们在两种条件下评估PHUMA：（1）对自录测试视频中未见运动的模仿；（2）仅基于骨盆引导的路径跟随。两种场景下，基于PHUMA训练的策略均优于Humanoid-X和AMASS，在多样化运动模仿方面取得显著提升。代码已开源：https://davian-robotics.github.io/PHUMA。

English

Motion imitation is a promising approach for humanoid locomotion, enabling agents to acquire humanlike behaviors. Existing methods typically rely on high-quality motion capture datasets such as AMASS, but these are scarce and expensive, limiting scalability and diversity. Recent studies attempt to scale data collection by converting large-scale internet videos, exemplified by Humanoid-X. However, they often introduce physical artifacts such as floating, penetration, and foot skating, which hinder stable imitation. In response, we introduce PHUMA, a Physically-grounded HUMAnoid locomotion dataset that leverages human video at scale, while addressing physical artifacts through careful data curation and physics-constrained retargeting. PHUMA enforces joint limits, ensures ground contact, and eliminates foot skating, producing motions that are both large-scale and physically reliable. We evaluated PHUMA in two sets of conditions: (i) imitation of unseen motion from self-recorded test videos and (ii) path following with pelvis-only guidance. In both cases, PHUMA-trained policies outperform Humanoid-X and AMASS, achieving significant gains in imitating diverse motions. The code is available at https://davian-robotics.github.io/PHUMA.

PHUMA：基于物理的人形机器人运动数据集

PHUMA: Physically-Grounded Humanoid Locomotion Dataset

摘要

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