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.

本文介绍了 Sim-MEES，一个包含 1550 个对象，具有不同难度水平和物理属性的大规模合成数据集，以及 1100 万个抓握标签，可用于移动机械手在混乱环境中使用不同抓取器模式进行抓握计划。通过将分析模型和整个混乱环境的动态模拟相结合，提供准确的抓标签并对其进行详细研究。研究比较并证明了 Sim-MEES 如何在混乱环境中提供精确的抓标签，包括平行夹具夹爪和吸盘夹爪。