Sim-Grasp: Learning 6-DOF Grasp Policies for Cluttered Environments Using a Synthetic Benchmark

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. Sim-Grasp is a deep-learning based system to determine the robust 6-DOF two-finger grasp poses in cluttered environments. I. INTRODUCTION Robotic grasping is a fundamental problem in robotics various grasping mechanisms, the two-finger gripper, also research, focusing on the manipulation of objects with known as a parallel jaw gripper, has garnered significant varying shapes and sizes in diverse environments.