论文标题
egad！机器人操纵的多样性和可重复性的进化抓写分析数据集
EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation
论文作者
论文摘要
我们介绍了进化的握把分析数据集（EGAD），其中包括2000多个生成的对象，该对象旨在训练和评估机器人视觉掌握检测算法。与其他用于机器人握把的数据集相比，Egad中的对象几何多样，从简单到复杂的形状到复杂形状，从易于到掌握，从简单到复杂的形状，从易于掌握到难度，该数据集的大小可能限制或仅包含少量的对象类。此外，我们指定了一组49种不同的3D打印评估对象，以鼓励对一系列复杂性和难度进行机器人抓钩系统的可再现测试。数据集，代码和视频可在https://dougsm.github.io/egad/上找到
We present the Evolved Grasping Analysis Dataset (EGAD), comprising over 2000 generated objects aimed at training and evaluating robotic visual grasp detection algorithms. The objects in EGAD are geometrically diverse, filling a space ranging from simple to complex shapes and from easy to difficult to grasp, compared to other datasets for robotic grasping, which may be limited in size or contain only a small number of object classes. Additionally, we specify a set of 49 diverse 3D-printable evaluation objects to encourage reproducible testing of robotic grasping systems across a range of complexity and difficulty. The dataset, code and videos can be found at https://dougsm.github.io/egad/
