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/
@article{arxiv.2003.01314,
title = {EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation},
author = {Douglas Morrison and Peter Corke and Jürgen Leitner},
journal= {arXiv preprint arXiv:2003.01314},
year = {2020}
}
Comments
IEEE Robotics and Automation Letters (RA-L). Preprint Version. Accepted April, 2020. The dataset, code and videos can be found at https://dougsm.github.io/egad/