ACRONYM: A Large-Scale Grasp Dataset Based on Simulation [article]

Clemens Eppner, Arsalan Mousavian, Dieter Fox
2020 arXiv   pre-print
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
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arXiv:2011.09584v1 fatcat:ifkmxwosbfgihh5vutcdylgvei