ICRA 2021poster228 citations
ACRONYM: A Large-Scale Grasp Dataset Based on Simulation
Clemens Eppner, Arsalan Mousavian, Dieter Fox
Abstract
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. Data and tools can be accessed at https://sites.google.com/nvidia.com/graspdataset.
BibTeX
@inproceedings{icra2021_acronymalargesca,
title = {ACRONYM: A Large-Scale Grasp Dataset Based on Simulation},
author = {Clemens Eppner and Arsalan Mousavian and Dieter Fox},
booktitle = {ICRA 2021},
year = {2021}
}