UniFucGrasp: Human-Hand-Inspired Unified Functional Grasp Annotation Strategy and Dataset for Diverse Dexterous Hands
Haoran Lin, Wenrui Chen, Xianchi Chen, Fan Yang, Qiang Diao, Wenxin Xie, Sijie Wu, Kailun Yang, Maojun Li, Yaonan Wang
AI summary
Problem
Existing functional grasp datasets are restricted to expensive, fully-actuated robotic hands, creating high annotation costs and poor generalization across different hand architectures.
Approach
The authors introduce a biomimetic mapping framework that translates natural human hand motions into control commands for heterogeneous robotic hands using sparse matrix optimization and force-closure analysis.
Key results
- Unified human-to-robot pose mapping bridging structural and actuation differences
- UniFucGrasp dataset with 100K+ functional grasp annotations across 1,108 objects
- Point cloud-conditioned gesture generation model for multi-hand grasping
- Improved manipulation accuracy, stability, and cross-hand generalization in simulation and real-world tests
Why it matters
It provides a scalable, hardware-agnostic pipeline and dataset to accelerate adaptive dexterous manipulation research and reduce annotation bottlenecks.
Abstract
Dexterous grasp datasets are vital for embodied intelligence, but mostly emphasize grasp stability, ignoring func- tional grasps needed for tasks like opening bottle caps or holding cup handles. Most rely on bulky, costly, and hard- to-control high-DOF Shadow Hands. Inspired by the human hand’s underactuated mechanism, we establish UniFucGrasp, a universal functional grasp annotation strategy and dataset for multiple dexterous hand types. Based on biomimicry, it maps natural human motions to diverse hand structures and uses geometry-based force closure to ensure functional, stable, human- like grasps. This method supports low-cost, efficient collection of diverse, high-quality functional grasps. Finally, we establish the first multi-hand functional grasp dataset and provide a synthesis model to validate its effectiveness. Experiments on the UFG dataset, IsaacSim, and complex robotic tasks show that our method improves functional manipulation accuracy and grasp stability, demonstrates improved adaptability across multiple robotic hands, helping to alleviate annotation cost and generalization challenges in dexterous grasping. The project page is at https://haochen611.github.io/UFG.