ImDy: 模倣観測からの人間逆動力学
ImDy: Human Inverse Dynamics from Imitated Observations
人間の動作模倣アルゴリズムと物理シミュレータを組み合わせて大規模な逆動力学データセットImDyを構築し、関節トルクと床反力を同時推定するデータ駆動型ソルバを学習した研究。
著者: Xinpeng Liu, Junxuan Liang, Zili Lin, Haowen Hou, Yong-Lu Li, Cewu Lu
分類: cs.AI, cs.CV, cs.GR, cs.RO
原文アブストラクト
Inverse dynamics (ID), which aims at reproducing the driven torques from human kinematic observations, has been a critical tool for gait analysis. However, it is hindered from wider application to general motion due to its limited scalability. Conventional optimization-based ID requires expensive laboratory setups, restricting its availability. To alleviate this problem, we propose to exploit the recently progressive human motion imitation algorithms to learn human inverse dynamics in a data-driven manner. The key insight is that the human ID knowledge is implicitly possessed by motion imitators, though not directly applicable. In light of this, we devise an efficient data collection pipeline with state-of-the-art motion imitation algorithms and physics simulators, resulting in a large-scale human inverse dynamics benchmark as Imitated Dynamics (ImDy). ImDy contains over 150 hours of motion with joint torque and full-body ground reaction force data. With ImDy, we train a data-driven human inverse dynamics solver ImDyS(olver) in a fully supervised manner, which conducts ID and ground reaction force estimation simultaneously. Experiments on ImDy and real-world data demonstrate the impressive competency of ImDyS in human inverse dynamics and ground reaction force estimation. Moreover, the potential of ImDy(-S) as a fundamental motion analysis tool is exhibited with downstream applications. The project page is https://foruck.github.io/ImDy/.
