用刚体模型还原真人手部动作，支持实时物理仿真与数字孪生控制。
Multi-Rigid-Body Approximation of Human Hands with Application to Digital Twin
- 基于光学捕捉数据构建个性化手部刚体模型，保持解剖结构一致。
- 提出闭式解与BCH修正迭代法，精准映射自由旋转到受约束关节。
- 可实现亚厘米级重建误差，适用于机器人操控与虚拟人手模拟。
人体手部仿真在数字孪生应用中至关重要，需兼顾解剖真实性与计算效率。本文提出一套完整的多刚体手部建模流程，可在保持真实外观的同时实现实时物理仿真。从特定个体的手部光学动捕数据出发，构建个性化MANO模型，并转换为具有解剖学一致关节轴的URDF表示。核心挑战在于将MANO中无约束的SO(3)关节旋转投影到刚体模型的运动学约束关节上。针对单自由度关节，推导出闭式解；针对双自由度关节，提出基于Baker-Campbell-Hausdorff（BCH）修正的迭代方法，有效处理旋转非交换性问题。通过数字孪生实验验证：强化学习策略驱动多刚体手部重现捕获的人体动作。定量评估显示重建误差小于1厘米，且在多种操作任务中成功完成抓取。
原文摘要 · Abstract (English)
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.
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