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

arXiv: https://arxiv.org/abs/2403.09841 · HTML: https://mlsanity.com/paper/2403.09841/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2024.

## Built on by (most cited first, 16 of 16)

- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training](https://mlsanity.com/paper/2507.13097/) (2025)
- [Multi-GraspLLM: A Multimodal LLM for Multi-Hand Semantic Guided Grasp Generation](https://mlsanity.com/paper/2412.08468/) (2024)
- [OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework](https://mlsanity.com/paper/2503.01074/) (2025)
- [V-HOP: Visuo-Haptic 6D Object Pose Tracking](https://mlsanity.com/paper/2502.17434/) (2025)
- [RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands](https://mlsanity.com/paper/2408.11048/) (2024)
- [RobotFingerPrint: Unified Gripper Coordinate Space for Multi-Gripper Grasp Synthesis and Transfer](https://mlsanity.com/paper/2409.14519/) (2024)
- [MachaGrasp: Morphology-Aware Cross-Embodiment Dexterous Hand Articulation Generation for Grasping](https://mlsanity.com/paper/2510.06068/) (2025)
- [Grasp2Grasp: Vision-Based Dexterous Grasp Translation via Schr"odinger Bridges](https://mlsanity.com/paper/2506.02489/) (2025)
- [EAGG: Embodiment-Aligned Grasp Generation via Geometry-Aware Graph Conditioning](https://mlsanity.com/paper/2606.18092/) (2026)
- [LensDFF: Language-enhanced Sparse Feature Distillation for Efficient Few-Shot Dexterous Manipulation](https://mlsanity.com/paper/2503.03890/) (2025)
- [XGrasp: Gripper-Aware Grasp Detection with Multi-Gripper Data Generation](https://mlsanity.com/paper/2510.11036/) (2025)
- [Simulation-Driven Imitation Learning for Biosignals-Free Shared-Autonomy Prosthetic Grasping](https://mlsanity.com/paper/2606.07389/) (2026)
- [Beyond Visual Grasping: Benchmarking Complex Grasping from Detection to Execution](https://mlsanity.com/paper/2607.14341/) (2026)
- [MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping](https://mlsanity.com/paper/2608.02014/) (2026)
- [Gripper-aware Vision Language Action Models](https://mlsanity.com/paper/2608.24603/) (2026)

## Abstract (arXiv)

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