Manipulation

Grasp Detection

Grasp detection is the perception task of predicting viable grasp configurations directly from sensor data such as images, depth maps, or point clouds, without requiring a prior object model. Landmark systems include Dex-Net, which learned grasp robustness from millions of simulated grasps, GG-CNN for real-time pixel-wise grasp maps, and GraspNet and AnyGrasp for dense 6-DoF grasp proposals in clutter. Detected grasps are typically ranked by predicted quality and filtered for reachability.

Why it matters for physical AI

Model-free grasping of never-before-seen objects is the entry ticket to unstructured environments like homes and warehouses, and grasp detectors remain core components even inside pipelines led by end-to-end policies.