Abstract
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.
Abstract (translated)
在本文中，我们提出了Sim-Grasp系统，这是一个 robust 的6-DOF两指握持系统，集成了先进的语言模型以增强在复杂环境中的物体操作。我们引入了Sim-Grasp-Dataset，其中包括500个场景中1550个物体的79000个注释标签，并开发了Sim-GraspNet来生成点云中的抓持姿势。Sim-Grasp-Policies在单物体和混合复杂场景（级别1-2和级别3-4）中的抓持成功率为97.14%和87.43%和83.33%。通过通过文本和框提示集成目标识别语言模型，Sim-Grasp enabling both object-agnostic and target picking，推动了智能机器人系统的边界。
URL
https://arxiv.org/abs/2405.00841
