PHUMA：基于物理的人形机器人运动数据集

摘要

動作模仿是實現仿人機器人運動的一種前景廣闊的方法，能使智能體習得類人行為。現有方法通常依賴AMASS等高質量動作捕捉數據集，但這類數據稀缺且成本高昂，限制了可擴展性與多樣性。近期研究嘗試通過轉化大規模網絡視頻（如Humanoid-X）來擴展數據收集規模，但常伴隨漂浮、穿透和滑步等物理偽影，影響模仿穩定性。為此，我們提出PHUMA——基於物理約束的仿人運動數據集，該數據集在利用大規模人類視頻的同時，通過精細化數據校準與物理約束重定向技術解決物理偽影問題。PHUMA強制關節活動限制、確保足部接地並消除滑步現象，生成兼具大規模與物理可靠性的運動數據。我們在兩種情境下評估PHUMA：（1）對自錄測試視頻中未見過動作的模仿；（2）僅憑骨盆引導的路徑追蹤。兩種實驗中，基於PHUMA訓練的策略均超越Humanoid-X和AMASS，在模仿多樣化運動方面取得顯著提升。項目代碼已開源於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

Support