通过几何聚类选择专家，实现自然且高成功率的灵巧抓握。
GES-UniGrasp: A Two-Stage Dexterous Grasping Strategy With Geometry-Based Expert Selection
- 基于物体几何形状聚类，分阶段选择专用抓握策略。
- 在训练集和测试集上分别达到99.4%和96.3%的成功率。
- 适合需要真实感与泛化能力的机器人抓取任务。
在真实场景中实现鲁棒且类人化的通用物体灵巧抓取，是推动智能机器人操作发展的关键。然而，现有基于抓握先验的强化学习方法常导致不自然行为。本文提出《ContactGrasp》数据集，明确包含任务相关的腕部朝向与拇指食指捏合协调信息，涵盖82类共773个物体，为训练类人抓握策略提供丰富基础。在此基础上，我们采用几何聚类对物体按形状分组，构建两阶段几何专家选择（GES）框架，从多个专用专家中挑选适配不同几何形状的策略，显著提升对多样形状的适应性与跨类别泛化能力。实验表明，该方法能生成自然抓握姿态，在训练集和测试集上分别取得99.4%和96.3%的成功率，展现出优异的泛化性能与高质量抓握执行效果。
原文摘要 · Abstract (English)
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.
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