1. [Publications](/publications) 2. ACRONYM: A Large-Scale Grasp Dataset Based on Simulation # ACRONYM: A Large-Scale Grasp Dataset Based on Simulation ![](/sites/default/files/styles/wide/public/publications/Screenshot%20from%202022-03-24%2014-51-36.png?itok=GE9uJ_an) We introduce ACRONYM, a dataset for robot grasp planning based on physics simulation. The dataset contains 17.7M parallel-jaw grasps, spanning 8872 objects from 262 different categories, each labeled with the grasp result obtained from a physics simulator. We show the value of this large and diverse dataset by using it to train two state-of-the-art learning-based grasp planning algorithms. Grasp performance improves significantly when compared to the original smaller dataset. ## Authors Clemens Eppner (NVIDIA) Arsalan Mousavian (NVIDIA) Dieter Fox (NVIDIA) ## Publication Date Sunday, May 30, 2021 ## Published in [2021 IEEE International Conference on Robotics and Automation (ICRA)](https://ieeexplore.ieee.org/document/9560844) ## Research Area [Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence) [Robotics](/research-area/robotics) ## External Links [ArXiv version](https://arxiv.org/abs/2011.09584) [Website](https://sites.google.com/nvidia.com/graspdataset) [GitHub](https://github.com/NVlabs/acronym) ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .