人間の手のマルチ剛体近似とデジタルツインへの応用
Multi-Rigid-Body Approximation of Human Hands with Application to Digital Twin
人間の手の動作を光学式モーションキャプチャから取り込み、MANOモデルを関節軸が解剖学的に整合したURDFに変換することで、リアルタイム物理シミュレーション可能なマルチ剛体近似を構築するパイプラインを提案した。
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著者: Bin Zhao, Yiwen Lu, Haohua Zhu, Xiao Li, Sheng Yi
分類: cs.RO, cs.GR
原文アブストラクト
Human hand simulation plays a critical role in digital twin applications, requiring models that balance anatomical fidelity with computational efficiency. We present a complete pipeline for constructing multi-rigid-body approximations of human hands that preserve realistic appearance while enabling real-time physics simulation. Starting from optical motion capture of a specific human hand, we construct a personalized MANO (Multi-Abstracted hand model with Neural Operations) model and convert it to a URDF (Unified Robot Description Format) representation with anatomically consistent joint axes. The key technical challenge is projecting MANO's unconstrained SO(3) joint rotations onto the kinematically constrained joints of the rigid-body model. We derive closed-form solutions for single degree-of-freedom joints and introduce a Baker-Campbell-Hausdorff (BCH)-corrected iterative method for two degree-of-freedom joints that properly handles the non-commutativity of rotations. We validate our approach through digital twin experiments where reinforcement learning policies control the multi-rigid-body hand to replay captured human demonstrations. Quantitative evaluation shows sub-centimeter reconstruction error and successful grasp execution across diverse manipulation tasks.
