把持計画arXiv:2011.09584
ACRONYM：物理シミュレーションに基づく大規模把持データセット
ACRONYM: A Large-Scale Grasp Dataset Based on Simulation
物理シミュレーションで生成した1770万件のパラレルジョー把持データセットを構築し、学習ベース把持計画の性能向上を示した。
著者: Clemens Eppner, Arsalan Mousavian, Dieter Fox
分類: cs.RO, cs.CV
原文アブストラクト
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.
