Multi-Rigid-Body Approximation of Human Hands with Application to Digital Twin
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917132822904832 |
|---|---|
| author | Zhao, Bin Lu, Yiwen Zhu, Haohua Li, Xiao Yi, Sheng |
| author_facet | Zhao, Bin Lu, Yiwen Zhu, Haohua Li, Xiao Yi, Sheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07359 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Rigid-Body Approximation of Human Hands with Application to Digital Twin Zhao, Bin Lu, Yiwen Zhu, Haohua Li, Xiao Yi, Sheng Robotics Graphics 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. |
| title | Multi-Rigid-Body Approximation of Human Hands with Application to Digital Twin |
| topic | Robotics Graphics |
| url | https://arxiv.org/abs/2512.07359 |