Manipulation

Grasp Affordance

A grasp affordance is the property of an object or object region that indicates how it can be grasped, grounding Gibson's ecological notion of affordance in manipulation: handles afford wrapping, rims afford pinching, flat faces afford suction. Computationally, affordance models predict dense per-point or per-pixel graspability and often task-conditioned regions, learned from demonstration, simulation, or large visual datasets, and increasingly from vision-language models that link affordances to semantics.

Why it matters for physical AI

Task-aware grasping requires knowing not just where a grasp is stable but which grasp serves the goal, such as holding a knife by the handle to cut; affordance prediction bridges perception and purposeful manipulation.