# SynManDex: Synthesizing Human-like Dexterous Grasps from Synthetic Human Pre-Grasps

arXiv: https://arxiv.org/abs/2606.09798 · HTML: https://mlsanity.com/paper/2606.09798/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2026.

## Built on by (most cited first, 1 of 1)

- [Weave: Learning Whole-Body Dexterous Loco-Manipulation from Human-Object Interactions](https://mlsanity.com/paper/2609.16683/) (2026)

## Abstract (arXiv)

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.