提出通用功能抓取标注策略，支持多类灵巧手低成本高效采集高质量抓取数据。
UniFucGrasp: Human-Hand-Inspired Unified Functional Grasp Annotation Strategy and Dataset for Diverse Dexterous Hands
- 基于人体手部仿生机制，用几何力闭合实现自然、稳定的功能性抓取。
- 构建首个支持多类灵巧手的功能抓取数据集，提升抓取准确率与稳定性。
- 适用于机器人灵巧操作研究，降低标注成本，适合多手适配场景。
灵巧抓取数据集对具身智能至关重要，但现有数据集多关注抓取稳定性，忽视如开瓶盖、握杯把等任务所需的功能性抓取。多数依赖高自由度、昂贵且难控制的影子手（Shadow Hands）。受人手被动驱动机制启发，我们提出UniFucGrasp——一种通用功能抓取标注策略与数据集，适用于多种灵巧手类型。该方法基于仿生学，将自然人类动作映射至不同手结构，并采用基于几何的力闭合确保功能性强、稳定且类人抓取。该策略支持低成本、高效率采集多样化、高质量功能抓取数据。最终，我们建立了首个多手功能抓取数据集，并提供合成模型验证其有效性。在UFG数据集、IsaacSim及复杂机器人任务上的实验表明，该方法显著提升功能性操作准确率与抓取稳定性，增强跨多类机器人的适应性，有效缓解灵巧抓取中的标注成本与泛化难题。
原文摘要 · Abstract (English)
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.
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