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We introduce ACRONYM, a dataset for robot grasp planning based on physicssimulation. The dataset contains 17.7M parallel-jaw grasps, spanning 8872objects from 262 different categories, each labeled with the grasp resultobtained from a physics simulator. We show the value of this large and diversedataset by using it to train two state-of-the-art learning-based grasp planningalgorithms. Grasp performance improves significantly when compared to theoriginal smaller dataset. Data and tools can be accessed athttps://sites.google.com/nvidia.com/graspdataset.

ACRONYM: A Large-Scale Grasp Dataset Based on Simulation