GES-UniGrasp：一种采用基于几何的专家选择的两阶段灵巧抓取策略
GES-UniGrasp: A Two-Stage Dexterous Grasping Strategy With Geometry-Based Expert Selection
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中文总结 AI 辅助
本文构建涵盖82类773个物体的 ContactGrasp 灵巧抓取数据集，并提出基于几何聚类选择专门专家的两阶段 GES 框架，实现类人抓取与高泛化成功率。
中文摘要 AI 辅助
对一般物体进行鲁棒且类人的灵巧抓取，是在真实场景中推进智能机器人操作的一项关键能力。然而，现有由抓取先验引导的强化学习方法往往会产生不自然的行为。在本工作中，我们提出了 ContactGrasp，这是一个机器人灵巧预抓取与抓取数据集，明确考虑了与任务相关的手腕朝向以及拇指—食指捏取协调。该数据集涵盖82个类别的773个物体，为训练类人抓取策略提供了丰富基础。基于该数据集，我们执行基于几何的聚类，按形状对物体分组，从而实现两阶段的 Geometry-based Expert Selection（GES，基于几何的专家选择）框架；该框架在多个专门专家之间进行选择，以抓取具有不同几何形状的物体，从而增强对多样形状的适应性和跨类别泛化能力。我们的方法展现出自然的抓取姿态，并在训练集和测试集上分别取得99.4%和96.3%的高成功率，显示出强大的泛化能力和高质量的抓取执行。
英文摘要
Robust and human-like dexterous grasping of general objects is a critical capability for advancing intelligent robotic manipulation in real-world scenarios. However, existing reinforcement learning methods guided by grasp priors often result in unnatural behaviors. In this work, we present \textit{ContactGrasp}, a robotic dexterous pre-grasp and grasp dataset that explicitly accounts for task-relevant wrist orientation and thumb-index pinching coordination. The dataset covers 773 objects in 82 categories, providing a rich foundation for training human-like grasp strategies. Building upon this dataset, we perform geometry-based clustering to group objects by shape, enabling a two-stage Geometry-based Expert Selection (GES) framework that selects among specialized experts for grasping diverse object geometries, thereby enhancing adaptability to diverse shapes and generalization across categories. Our approach demonstrates natural grasp postures and achieves high success rates of 99.4\% and 96.3\% on the train and test sets, respectively, showcasing strong generalization and high-quality grasp execution.
