标题： DexVLG：大规模灵活视觉-语言-抓取模型 显示英文标题

标题： DexVLG: Dexterous Vision-Language-Grasp Model at Scale

摘要： 随着大型模型的兴起，视觉-语言-动作（VLA）系统正在使机器人能够处理越来越复杂的任务。 然而，由于数据收集的难度，进展主要集中在控制简单的夹爪末端执行器上。 关于使用大型模型进行类人灵巧手的功能性抓取的研究很少。 在本文中，我们引入了 DexVLG，一个大型视觉-语言-抓取模型，用于使用单视角RGBD输入与语言指令对齐的灵巧抓取姿态预测。 为了完成这项工作，我们生成了一个包含1.7亿个灵巧抓取姿态的数据集，这些姿态映射到174,000个物体的语义部分，并配有详细的部件级描述。 这个大规模数据集，名为DexGraspNet 3.0，用于训练一个视觉语言模型和基于流匹配的抓取姿态头部，能够为桌面物体生成与指令对齐的抓取姿态。 为了评估DexVLG的性能，我们创建了基于物理的模拟基准并进行了现实世界实验。 大量测试表明DexVLG具有强大的零样本泛化能力——在模拟中实现了超过76%的零样本执行成功率，并达到了最先进的部件抓取准确率，在现实世界场景中成功实现了部件对齐的抓取。 显示英文摘要

摘要： As large models gain traction, vision-language-action (VLA) systems are enabling robots to tackle increasingly complex tasks. However, limited by the difficulty of data collection, progress has mainly focused on controlling simple gripper end-effectors. There is little research on functional grasping with large models for human-like dexterous hands. In this paper, we introduce DexVLG, a large Vision-Language-Grasp model for Dexterous grasp pose prediction aligned with language instructions using single-view RGBD input. To accomplish this, we generate a dataset of 170 million dexterous grasp poses mapped to semantic parts across 174,000 objects in simulation, paired with detailed part-level captions. This large-scale dataset, named DexGraspNet 3.0, is used to train a VLM and flow-matching-based pose head capable of producing instruction-aligned grasp poses for tabletop objects. To assess DexVLG's performance, we create benchmarks in physics-based simulations and conduct real-world experiments. Extensive testing demonstrates DexVLG's strong zero-shot generalization capabilities-achieving over 76% zero-shot execution success rate and state-of-the-art part-grasp accuracy in simulation-and successful part-aligned grasps on physical objects in real-world scenarios.