BiDexGrasp: 跨物体几何与尺寸的协调双臂灵巧抓取
BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes
AI总结：
本文提出BiDexGrasp，包含大规模双臂灵巧抓取数据集和新型生成模型，通过高效区域基抓取初始化与解耦力闭合优化策略，生成协调且高质量的抓取。
摘要：
双臂灵巧抓取是机器人领域基础且有前景的研究方向，但其进展受限于缺乏全面数据集和强大生成模型。本文提出BiDexGrasp，包含大规模双臂灵巧抓取数据集和新型生成模型。针对数据集，我们提出一种新的双臂抓取合成管道，通过两阶段合成策略高效标注物理可行数据。该管道通过高效区域基抓取初始化和解耦力闭合抓取优化策略解决高维双臂抓取挑战。基于此管道，我们构建了一个包含6351种不同物体（尺寸从30到80厘米）和970万条标注抓取数据的大规模双臂灵巧抓取数据集。基于此数据集，我们进一步引入了双臂协调和几何尺寸自适应的灵巧抓取生成框架。该框架有两个关键设计：双臂协调模块和几何尺寸自适应抓取生成策略，用于生成协调且高质量的抓取。在仿真和现实世界中进行的广泛实验验证了所提数据合成管道和学习生成框架的优越性能。
英文摘要：
Bimanual dexterous grasping is a fundamental and promising area in robotics, yet its progress is constrained by the lack of comprehensive datasets and powerful generation models. In this work, we propose BiDexGrasp, consisting of a large-scale bimanual dexterous grasp dataset and a novel learning-based framework. For dataset construction, we propose a novel bimanual grasp synthesis pipeline to efficiently annotate physically feasible data. This pipeline addresses the challenges of high-dimensional bimanual grasping through a two-stage synthesis strategy of efficient region-based grasp initialization and decoupled force-closure grasp optimization. Powered by this pipeline, we construct a large-scale bimanual dexterous grasp dataset, comprising 6351 diverse objects with sizes ranging from 30 to 80 cm, along with 9.53 million annotated grasp data. Based on this dataset, we further introduce a novel learning-based dexterous grasping generation framework. The framework lies in two key designs: a bimanual coordination module and a geometry-size-adaptive grasp generation strategy to generate coordinated and high-quality grasps on unseen objects. Extensive experiments conducted in both simulation and real world demonstrate the superior performance of our proposed data synthesis pipeline and learned generative framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
