HUGS: Guiding Unified Dexterous Grasp Synthesis Across Modes and Scales via Learned Human Priors

2026-07-05

AI Summary

Existing dexterous grasp synthesis methods struggle to simultaneously achieve high success rates and diverse contact patterns across objects of varying scales. To address this challenge, this work proposes the HUGS framework, which innovatively leverages a small-scale human grasp dataset to learn a conditional prior that adaptively recommends both contact modes and initial wrist poses. Integrated with force-closure-aware optimization, HUGS unifies the generation of multimodal grasps—ranging from precision pinches to bimanual coordination. The approach overcomes limitations of traditional heuristic strategies, synthesizing 3.2 million grasp samples across 157,000 scenes covering objects from 2 to 30 cm in size. Real-world experiments demonstrate that the system autonomously selects appropriate contact modes, successfully grasping diverse objects—from small screws to large boxes—with high reliability.

Abstract

Dexterous grasping across diverse object scales requires contact modes ranging from two-finger pinches to bimanual grasps. Existing dexterous grasp synthesis methods reduce the high-dimensional optimization space with manually designed expected contacts and initialization heuristics, which struggle to balance synthesis success rate and diversity. We present HUGS (Human-prior-guided Unified Dexterous Grasp Synthesis), a human-prior-guided framework for unified dexterous grasp synthesis across modes and scales. Instead of directly retargeting human demonstrations, HUGS learns an object-conditioned human prior that captures human grasp preferences and guides downstream force-closure-aware optimization. The prior is trained on a compact self-collected human grasp dataset with 1.8K grasps over 304 objects, providing broad coverage of object scales and contact modes. During synthesis, HUGS adaptively proposes contact modes and wrist initializations, substantially improving the balance between contact-mode coverage and synthesis success rate over heuristic-based methods. With HUGS, we synthesize 3.2M robotic grasps over 157K scenes, spanning object half-diagonal lengths from 2 cm to 30 cm and modes from two-finger to bimanual grasps. Models trained on the synthesized dataset autonomously select appropriate contact modes in the real world, enabling grasping from screws to large boxes.

Problem

• dexterous grasping
• grasp synthesis
• contact modes
• object scales
• grasp diversity

Innovation

• dexterous grasp synthesis
• human prior
• contact mode adaptation
• force-closure-aware optimization
• unified grasping