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Sim-MEES: Modular End-Effector System Grasping Dataset for Mobile Manipulators in Cluttered Environments

arXiv 2305.10580 ↗First posted 17 May 2023cs.RO cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 45 references that are in the map. First posted on arXiv 17 May 2023.

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20 of 45 references.

References · 20

  1. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  2. 20196-DOF GraspNet: Variational Grasp Generation for Object Manipulationcited by 242
  3. 2017Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matchingcited by 125
  4. 2020ACRONYM: A Large-Scale Grasp Dataset Based on Simulationcited by 117
  5. 2017Grasp Pose Detection in Point Cloudscited by 111
  6. 2013Data-Driven Grasp Synthesis - A Surveycited by 106
  7. 2015Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocolscited by 99
  8. 2018GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learningcited by 90
  9. 2018Jacquard: A Large Scale Dataset for Robotic Grasp Detectioncited by 87
  10. 2018PointNetGPD: Detecting Grasp Configurations from Point Setscited by 80
  11. 2014Real-Time Grasp Detection Using Convolutional Neural Networkscited by 76
  12. 2016High precision grasp pose detection in dense cluttercited by 34
8 more
  1. 2021SuctionNet-1Billion: A Large-Scale Benchmark for Suction Graspingcited by 32
  2. 2019A Billion Ways to Grasp: An Evaluation of Grasp Sampling Schemes on a Dense, Physics-based Grasp Data Setcited by 30
  3. 2018RMPflow: A Computational Graph for Automatic Motion Policy Generationcited by 21
  4. 2021GPR: Grasp Pose Refinement Network for Cluttered Scenescited by 13
  5. 2019A Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homescited by 12
  6. 2020Real-time Grasp Pose Estimation for Novel Objects in Densely Cluttered Environmentcited by 5
  7. 2021Learning suction graspability considering grasp quality and robot reachability for bin-pickingcited by 5
  8. 2019Object-Agnostic Suction Grasp Affordance Detection in Dense Cluster Using Self-Supervised Learning.docxcited by 2

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