2020/03/02 by Douglas Morrison, D.R.O. Morrison, Morrison, Douglas +4 · 12 citations
Computer Science · Engineering · #FOS: Computer and information sciences #Human Pose and Action Recognition #Multimodal Machine Learning Applications #Robot Manipulation and Learning #Robotics (cs.RO) #cs.RO
paper · pdf · doi:10.48550/arxiv.2003.01314
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/
openalex publication_date 2020/03/02 · arxiv created 2020/04/23 · arxiv updated 2020/04/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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/