EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation
This addresses the need for standardized and varied datasets in robotic manipulation research, though it is incremental as it builds on existing data collection efforts.
The authors tackled the problem of limited diversity and reproducibility in robotic grasping datasets by creating EGAD, which includes over 2000 geometrically diverse objects and a set of 49 3D-printable evaluation objects, enabling more comprehensive training and testing of visual grasp detection algorithms.
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/