Data & Benchmarks

GraspNet

GraspNet, specifically GraspNet-1Billion from Fang et al. (2020), is a large-scale benchmark for general object grasping comprising about 97,000 RGB-D images of cluttered scenes with over one billion densely annotated 6-DoF grasp poses, plus a standardized evaluation protocol scoring predicted grasps by analytic quality. The accompanying baseline networks and successors such as AnyGrasp made it a reference point for clutter grasp detection research.

Why it matters for physical AI

Dense, standardized grasp benchmarks let the community measure real progress in clutter picking rather than compare demos, and pretrained detectors from such datasets ship inside many practical picking stacks.