In this paper, we present Sim-MEES: a large-scale synthetic dataset that contains 1,550 objects with varying difficulty levels and physics properties, as well as 11 million grasp labels for mobile manipulators to plan grasps using different gripper modalities in cluttered environments. Our dataset generation process combines analytic models and dynamic simulations of the entire cluttered environment to provide accurate grasp labels. We provide a detailed study of our proposed labeling process for both parallel jaw grippers and suction cup grippers, comparing them with state-of-the-art methods to demonstrate how Sim-MEES can provide precise grasp labels in cluttered environments.

翻译：本文提出Sim-MEES：一个大规模合成数据集，包含1,550个具有不同难度等级与物理属性的物体，以及1,100万个抓取标签，用于移动操作器在杂乱环境中使用不同夹爪模式规划抓取。我们的数据集生成流程结合解析模型与整个杂乱环境的动态仿真，以提供精确的抓取标签。我们针对平行颚夹爪与吸盘夹爪，对所提出的标注流程进行了详细研究，并与前沿方法进行了对比，以展示Sim-MEES如何在杂乱环境中提供精准抓取标签。