# UniFucGrasp: Human-Hand-Inspired Unified Functional Grasp Annotation Strategy and Dataset for Diverse Dexterous Hands

arXiv: https://arxiv.org/abs/2508.03339 · HTML: https://mlsanity.com/paper/2508.03339/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 13 of 13)

- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023)
- [DexYCB: A Benchmark for Capturing Hand Grasping of Objects](https://mlsanity.com/paper/2104.04631/) (2021)
- [Deep Closest Point: Learning Representations for Point Cloud Registration](https://mlsanity.com/paper/1905.03304/) (2019)
- [DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation](https://mlsanity.com/paper/2210.02697/) (2022)
- [OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction](https://mlsanity.com/paper/2203.15709/) (2022)
- [Synthesizing Diverse and Physically Stable Grasps with Arbitrary Hand Structures using Differentiable Force Closure Estimator](https://mlsanity.com/paper/2104.09194/) (2021)
- [Dex1B: Learning with 1B Demonstrations for Dexterous Manipulation](https://mlsanity.com/paper/2506.17198/) (2025)
- [DexVLG: Dexterous Vision-Language-Grasp Model at Scale](https://mlsanity.com/paper/2507.02747/) (2025)
- [Vision-based Teleoperation of Shadow Dexterous Hand using End-to-End Deep Neural Network](https://mlsanity.com/paper/1809.06268/) (2018)
- [Generating Grasp Poses for a High-DOF Gripper Using Neural Networks](https://mlsanity.com/paper/1903.00425/) (2019)
- [DVGG: Deep Variational Grasp Generation for Dextrous Manipulation](https://mlsanity.com/paper/2211.11154/) (2022)
- [Intuitive Hand Teleoperation by Novice Operators Using a Continuous Teleoperation Subspace](https://mlsanity.com/paper/1802.04349/) (2018)

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

- [EAGG: Embodiment-Aligned Grasp Generation via Geometry-Aware Graph Conditioning](https://mlsanity.com/paper/2606.18092/) (2026)

## Abstract (arXiv)

Dexterous grasp datasets are vital for embodied intelligence, but mostly emphasize grasp stability, ignoring functional 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.