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MultiGripperGrasp: A Dataset for Robotic Grasping from Parallel Jaw Grippers to Dexterous Hands

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arxiv 2403.09841 v2 pith:7EDXBY54 submitted 2024-03-14 cs.RO

Luis Felipe Casas , Ninad Khargonkar , Balakrishnan Prabhakaran , Yu Xiang This is my paper · ORCID
classification cs.RO
keywords datasetgrippersgraspgraspsmultigrippergrasptransfergraspinggripper
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce a large-scale dataset named MultiGripperGrasp for robotic grasping. Our dataset contains 30.4M grasps from 11 grippers for 345 objects. These grippers range from two-finger grippers to five-finger grippers, including a human hand. All grasps in the dataset are verified in the robot simulator Isaac Sim to classify them as successful and unsuccessful grasps. Additionally, the object fall-off time for each grasp is recorded as a grasp quality measurement. Furthermore, the grippers in our dataset are aligned according to the orientation and position of their palms, allowing us to transfer grasps from one gripper to another. The grasp transfer significantly increases the number of successful grasps for each gripper in the dataset. Our dataset is useful to study generalized grasp planning and grasp transfer across different grippers. Data, code and videos for the project are available at https://irvlutd.github.io/MultiGripperGrasp

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training

    cs.RO 2025-07 conditional novelty 6.0 of 10

    GraspGen shows that training a grasp-scoring discriminator on the generator's own simulated outputs, plus a large new multi-gripper dataset, improves 6-DOF grasping across simulation and a real robot.

  2. Multi-GraspLLM: A Multimodal LLM for Multi-Hand Semantic Guided Grasp Generation

    cs.RO 2024-12 conditional novelty 6.0 of 10

    Multi-GraspLLM uses a single multimodal LLM, trained on a new 140k-grasp, 1.1M-dialogue dataset, to generate semantic grasp poses for five different robotic hands.

  3. SoGraB: A Visual Method for Soft Grasping Benchmarking and Evaluation

    cs.RO 2024-11 conditional novelty 5.0 of 10

    SoGraB scores soft grippers by measuring object deformation via Density-Aware Chamfer Distance between pre- and post-grasp point clouds.

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