An affordance is a possibility for action that the environment offers to a particular agent: a handle affords pulling, a mug affords grasping by its handle or rim. The concept comes from the psychologist James J. Gibson, who set it out in The Ecological Approach to Visual Perception (1979). The point most often lost is that affordances are relational. They depend on the agent's body as well as on the object: a shelf at shoulder height affords reaching to a humanoid but not to a tabletop arm whose workspace ends below it.
Robot learning uses the word in two concrete senses. The first is a learned function that scores whether a skill will succeed from the current state. SayCan (Ahn et al., "Do As I Can, Not As I Say: Grounding Language in Robotic Affordances", 2022) combined a language model's proposed steps with value functions of pretrained skills, which "provide the grounding necessary to connect this knowledge to a particular physical environment". The second is a spatial prediction, such as grasp points, contact regions or keypoints in an image, used as an intermediate representation between perception and control.
End-to-end VLAs such as SmolVLA and π0 do not output an explicit affordance map. Whatever they encode about affordances is implicit in the action distribution learned from demonstrations on specific robots.
A common misconception is that recognizing an object implies knowing how to act on it. Because affordances depend on kinematics, reach and gripper geometry, a policy can identify an object correctly and still fail to manipulate it on a different robot. That is one face of the embodiment gap, and it is why compatibility in this matrix is recorded per robot rather than per policy.