Paper Detail
Yanming Shao, Zanxin Chen, Wenwei Lin, Mingjie Zhou, Tianxing Chen, Xiaokang Yang, Yichen Chi, Yao Mu
Human hand-object interactions encode functional intent, but direct transfer to robotic hands often fails under morphology, contact, and reachability constraints. We present SynManDex, a synthetic pipeline that uses generated human pre-grasps as affordance-aware proposals and resolves the final contacts with robot-native optimization. SynManDex samples object-conditioned digital human pre-grasps, retargets them to dexterous robotic hand poses, optimizes force-closure contacts on the target embodiment, and admits trajectories that pass checks from each step. The resulting keyframes support both grasp-and-lift demonstrations and various prehensile manipulation tasks such as tea pouring, photo taking, and flute playing, designed via VLM agents. As a result, SynManDex combines high grasp quality (86.4\% grasp stability) with 4.67/5 human-likeness (93.4\%). It achieves 80.7\% successes in simulation and 25/30 (83.3\%) real-robot successes when applied to a 36-DOF bimanual dexterous robotic platform.
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@article{shao2026synmandex,
title = {SynManDex: Synthesizing Human-like Dexterous Grasps from Synthetic Human Pre-Grasps},
author = {Yanming Shao and Zanxin Chen and Wenwei Lin and Mingjie Zhou and Tianxing Chen and Xiaokang Yang and Yichen Chi and Yao Mu},
year = {2026},
abstract = {Human hand-object interactions encode functional intent, but direct transfer to robotic hands often fails under morphology, contact, and reachability constraints. We present SynManDex, a synthetic pipeline that uses generated human pre-grasps as affordance-aware proposals and resolves the final contacts with robot-native optimization. SynManDex samples object-conditioned digital human pre-grasps, retargets them to dexterous robotic hand poses, optimizes force-closure contacts on the target embod},
url = {https://arxiv.org/abs/2606.09798},
keywords = {cs.RO},
eprint = {2606.09798},
archiveprefix = {arXiv},
}
{}