EGAD!
EGAD! (the Evolved Grasping Analysis Dataset) uses evolutionary algorithms to generate a set of objects that spans the space of geometric complexity and grasp difficulty, plus a 49-object 3D-printable evaluation set.
It is a diagnostic tool rather than a leaderboard: it tells you where a grasping method fails along the geometry/difficulty axes, not a single headline score.
Primary source →- Source
- Morrison et al., RA-L 2020
- Year
- 2020
- Scale
- 2,000+ evolved objects · 49 diverse 3D-printable eval objects
- Gripper
- Parallel-jaw
- Modality
- Mesh · depth
- Best-known
- Diagnostic set (geometry × difficulty) · no single SOTA number
- Objects span geometric complexity × grasp difficulty
- 49 reproducible, 3D-printable evaluation objects
- Diagnostic — no single SOTA number by design
SIM = simulation result · HW = physical hardware. Image-wise accuracy is detection quality, not real-robot pick success. Figures cited from Morrison et al., RA-L 2020.
Related benchmarks
← Back to the grasping registerGraspNet-1Billion →
De-facto clutter benchmark · AnyGrasp current SOTA (AP)
Dex-Net 2.0 →
HW: 93% on adversarial · 99% precision on 40 novel objects (YuMi)
Grasp-Anything →
Language-driven grasp synthesis · open-vocabulary scenes
Jacquard →
~95% image-wise (GR-ConvNet-class)