  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/
