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

2026-06-08

AI Summary

This work addresses the challenge of directly transferring human grasping motions to dexterous robotic hands, which often fails due to morphological and contact constraints. To overcome this, the authors propose a novel approach that integrates functional-aware human pre-grasp synthesis with robot-native contact optimization. The method leverages object-conditioned digital human pre-grasp sampling, hand pose retargeting, and force-closure-based contact refinement, augmented by a vision-language model (VLM) agent for task-level planning to generate high-quality, anthropomorphic end-to-end manipulation demonstrations. Evaluated in simulation, the approach achieves an 80.7% success rate; on a real-world 36-degree-of-freedom bimanual robot platform, it completes 25 out of 30 tasks (83.3%) with 86.4% grasp stability and 93.4% anthropomorphic fidelity, marking the first effective fusion of human-inspired pre-grasps and robot-native contact optimization.

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 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.

Problem

• dexterous grasping
• human-to-robot transfer
• hand-object interaction
• grasp synthesis
• morphological constraints

Innovation

• dexterous grasping
• human-to-robot retargeting
• affordance-aware synthesis
• force-closure optimization
• pre-grasp generation