PHUMA：物理可靠的仿人运动数据集
PHUMA: Physically Reliable Humanoid Locomotion Dataset
AI总结：
本文提出PHUMA数据集，通过结合物理感知的筛选和物理约束的重定向，整合动作捕捉和网络视频，生成物理可靠的仿人运动数据，提升仿人运动的稳定性和泛化能力。
摘要：
运动模仿是实现仿人运动的一种有前景的方法，使代理能够获取类人行为。现有方法通常依赖高质量的动作捕捉数据集，如AMASS，但这些数据稀缺且昂贵，限制了可扩展性和多样性。最近的研究尝试通过转换大规模互联网视频来扩大数据收集，例如Humanoid-X。然而，这些方法常面临物理伪影，如漂浮、穿透和脚滑，阻碍了稳定的模仿。为此，我们引入PHUMA，一个物理可靠的仿人运动数据集，通过两阶段流程结合物理感知的筛选和物理约束的重定向，将动作捕捉和互联网视频整合为一个73小时的物理可靠数据集。在动作跟踪基准测试中，PHUMA训练的策略比AMASS和Humanoid-X训练的策略成功率更高，并成功在真实Unitree G1上实现零样本迁移。代码可在https://davian-robotics.github.io/PHUMA获取。
英文摘要：
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 suffer from physical artifacts such as floating, penetration, and foot skating, which hinder stable imitation. To address this, we introduce PHUMA, a Physically Reliable HUMAnoid locomotion dataset produced by a two-stage pipeline combining physics-aware curation and physics-constrained retargeting, aggregating both motion capture and internet video into a physically reliable, 73-hour corpus. On motion tracking benchmarks, PHUMA-trained policies achieve higher success rates than those trained on AMASS and Humanoid-X, and successfully transfer zero-shot to a real Unitree G1. The code is available at https://davian-robotics.github.io/PHUMA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
