EGAD! An Evolved Grasping Analysis Dataset for Diversity and Reproducibility in Robotic Manipulation

被引：63
|
作者：
Morrison, Douglas [1 ]
Corke, Peter [1 ]
Leitner, Jurgen [1 ,2 ]
机构：
[1] Queensland Univ Technol QUT, Australian Ctr Robot Vis ACRV, Brisbane, Qld 4000, Australia
[2] LYRO Robot, Brisbane, Qld 4113, Australia
来源：
IEEE ROBOTICS AND AUTOMATION LETTERS | 2020年 / 5卷 / 03期
基金：
澳大利亚研究理事会;
关键词：
Grasping; performance evaluation and benchmarking; deep learning in grasping and manipulation; NETWORKS;
D O I：
10.1109/LRA.2020.2992195
中图分类号：
TP24 [机器人技术];
学科分类号：
080202 ; 1405 ;
摘要：
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/
引用
收藏
页码：4368 / 4375
页数：8
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