论文标题
首字母缩写：基于模拟的大规模GRASP数据集
ACRONYM: A Large-Scale Grasp Dataset Based on Simulation
论文作者
论文摘要
我们介绍了基于物理模拟的机器人掌握计划的数据集，这是一个数据集。该数据集包含177m的并行jaw grasps，涵盖了来自262个不同类别的8872个对象，每个对象都标记为从物理模拟器获得的grasp结果。我们通过使用它来培训两个基于学习的GRASP计划算法来展示这个大型多样的数据集的价值。与原始较小的数据集相比，GRASP性能会显着提高。可以在https://sites.google.com/nvidia.com/graspdataset上访问数据和工具。
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.
