Sim-MEES: Modular End-Effector System Grasping Dataset for Mobile Manipulators in Cluttered Environments
This provides a dataset for robotics researchers working on mobile manipulation in cluttered settings, but it is incremental as it builds on existing synthetic data generation approaches.
The paper tackles the problem of generating accurate grasp labels for mobile manipulators in cluttered environments by presenting Sim-MEES, a large-scale synthetic dataset with 1,550 objects and 11 million grasp labels, which improves precision compared to state-of-the-art methods.
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.