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

页码：4368 / 4375

页数：8

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