Sim-MEES：面向杂乱环境中移动机械臂的模块化末端执行器系统抓取数据集
Sim-MEES: Modular End-Effector System Grasping Dataset for Mobile Manipulators in Cluttered Environments
AI总结：
本文提出大规模合成数据集Sim-MEES，包含1550个物体和1100万抓取标签，结合分析模型与动态仿真为平行夹爪和吸盘夹爪生成精确标签，助力移动机械臂在杂乱环境中规划抓取。
摘要：
本文提出了Sim-MEES：一个大规模合成数据集，包含1,550个具有不同难度级别和物理属性的物体，以及1100万个抓取标签，供移动机械臂在杂乱环境中使用不同夹爪模态规划抓取。我们的数据集生成过程结合了分析模型和整个杂乱环境的动态仿真，以提供准确的抓取标签。我们详细研究了对平行夹爪和吸盘夹爪提出的标记过程，并将其与最先进的方法进行比较，以证明Sim-MEES如何在杂乱环境中提供精确的抓取标签。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
