Sim-Grasp: Learning 6-DOF Grasp Policies for Cluttered Environments Using a Synthetic Benchmark
<|reference_start|>Sim-Grasp: Learning 6-DOF Grasp Policies for Cluttered Environments Using a Synthetic Benchmark: 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.<|reference_end|>
@article{li2024sim-grasp:,
    title={Sim-Grasp: Learning 6-DOF Grasp Policies for Cluttered Environments
  Using a Synthetic Benchmark},
    author={Juncheng Li and David J. Cappelleri},
    journal={IEEE Robotics and Automation Letters (2024) 1-8},
    year={2024},
    doi={10.1109/LRA.2024.3430712},
    archivePrefix={arXiv},
    eprint={2405.00841},
    primaryClass={cs.RO cs.AI}
}