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About this paper
Xiaoyi Lin, Kunpeng Yao, Lixin Xu, Xueqiang Wang, Xuetao Li, Yuchen Wang, Miao Li
DexFlow presents a unified method for translating human hand movements to robotic hands, ensuring both anatomical accuracy and physical plausibility. This approach improves grasp quality by reducing object penetration by 90% and contact state fluctuations by 68% compared to traditional retargeting methods.
Key insights
- A hierarchical optimization framework is employed to balance global pose establishment with fine-grained local contact refinement.
- Sequential finger optimization, starting with the thumb, is used to account for interdependencies between digits during grasping.
- Dual-threshold contact detection combined with frame-to-frame smoothing effectively identifies stable contact points, significantly reducing noisy fluctuations and object penetration.
Summary by alphaXiv.
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