In this paper, we present Sim-Grasp, a robust 6-DOF two-finger grasping system that integrates advanced language models for enhanced object manipulation in cluttered environments. We introduce the Sim-Grasp-Dataset, which includes 1,550 objects across 500 scenarios with 7.9 million annotated labels, and develop Sim-GraspNet to generate grasp poses from point clouds. The Sim-Grasp-Polices achieve grasping success rates of 97.14% for single objects and 87.43% and 83.33% for mixed clutter scenarios of Levels 1-2 and Levels 3-4 objects, respectively. By incorporating language models for target identification through text and box prompts, Sim-Grasp enables both object-agnostic and target picking, pushing the boundaries of intelligent robotic systems.

翻译：本文提出Sim-Grasp——一个鲁棒的六自由度双指抓取系统，该系统集成先进语言模型以增强杂乱环境下的物体操控能力。我们构建了包含1,550个物体、跨越500个场景、具有790万标注标签的Sim-Grasp-Dataset数据集，并开发了Sim-GraspNet模型用于从点云生成抓取位姿。Sim-Grasp-Polices策略在单一物体抓取中实现97.14%的成功率，在1-2级和3-4级混合杂乱场景中分别达到87.43%和83.33%的成功率。通过结合语言模型实现文本与框选提示的目标识别，Sim-Grasp同时支持物体无关抓取与目标选取，推动了智能机器人系统的发展边界。