DexMani: Human-Derived Manipulability Guidance for Dexterous Rotation
Dexterous object rotation is a sequential contact problem: each support, release, and re-contact decision must both produce the desired object motion, and prepare the hand configuration for continued rotation. Existing reinforcement learning methods discover such movement patterns through trial and error on specific robotic hand embodiments, without explicitly accounting for how each contact transition affects the hand's ability to sustain object rotation in subsequent steps. We introduce DexMani, a framework that transfers human demonstrations as contact-conditioned manipulability evolution. This prior captures how successful human contact transitions reshape the object-rotation directio...
| Authors | Xiaoyang Chen, Shengcheng Luo, Haoran Guo et al. |
|---|---|
| Primary category | cs.RO |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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