EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation
| Autor: | Peter Corke, Jürgen Leitner, Douglas Morrison |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Control and Optimization Computer science Biomedical Engineering 02 engineering and technology Computer Science - Robotics 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Computer vision Analysis Dataset Set (psychology) business.industry Mechanical Engineering GRASP Ranging Object (computer science) Computer Science Applications Human-Computer Interaction Range (mathematics) Control and Systems Engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Robotics (cs.RO) |
| DOI: | 10.48550/arxiv.2003.01314 |
| Popis: | 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/ Comment: 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/ |
| Databáze: | OpenAIRE |
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