Perception

Occupancy Map Learning

Occupancy map learning is the use of learned models to predict which regions of space are occupied, going beyond direct sensor ray-casting to infer occupancy in unobserved or occluded areas. Examples include neural implicit occupancy fields, semantic occupancy prediction from camera images in autonomous driving, and models that forecast how occupancy will evolve around dynamic agents.

Why it matters for physical AI

Predicting occupancy behind occlusions and ahead in time lets robots plan safely with incomplete sensing, a step beyond maps that only record what sensors have directly measured.