Grasp benchmark · Suction
SuctionNet-1Billion
suctionclutterseal modelreal RGB-D
SuctionNet-1Billion is the suction counterpart to GraspNet-1Billion, built on the same 190 cluttered scenes and 88 objects. It contributes a physically grounded suction model that analytically evaluates seal formation and wrench resistance at billion scale.
It provides a dedicated metric for suction grasping and a baseline network, filling the gap that most grasp benchmarks ignore suction entirely despite its dominance in real warehouses.
Primary source →At a glance
- Source
- Cao et al., RA-L 2021
- Year
- 2021
- Scale
- 190 scenes · 88 objects · 97,280 images · ~1.1B suction annotations
- Gripper
- Suction
- Modality
- RGB-D
- Best-known
- HW: 80.65% grasp success · 100% object clearance (their method)
Key results
- HW: 80.65% grasp success rate (their baseline method)
- HW: 100% object clearance on the evaluated scenes
- Analytic seal-formation + wrench-resistance scoring
SIM = simulation result · HW = physical hardware. Image-wise accuracy is detection quality, not real-robot pick success. Figures cited from Cao et al., RA-L 2021.