Perception

Active Perception

Active perception is the strategy of controlling a robot's sensors or body specifically to improve what it perceives, rather than passively processing whatever arrives. Examples include moving a wrist camera to disambiguate object pose, shifting gaze to reduce occlusion, and next-best-view planning for 3D reconstruction. Formalized by Bajcsy and colleagues in the 1980s, it treats perception as a closed-loop decision problem coupling sensing and action.

Why it matters for physical AI

Deployed manipulation systems constantly face occlusion and ambiguity, and policies that learn to reposition cameras or peek before grasping recover information that no amount of passive network capacity can supply.