  In realistic applications of object search, robots will need to locate target
objects in complex environments while coping with unreliable sensors,
especially for small or hard-to-detect objects. In such settings, correlational
information can be valuable for planning efficiently. Previous approaches that
consider correlational information typically resort to ad-hoc, greedy search
strategies. We introduce the Correlational Object Search POMDP (COS-POMDP),
which models correlations while preserving optimal solutions with a reduced
state space. We propose a hierarchical planning algorithm to scale up
COS-POMDPs for practical domains. Our evaluation, conducted with the AI2-THOR
household simulator and the YOLOv5 object detector, shows that our method finds
objects more successfully and efficiently compared to baselines,particularly
for hard-to-detect objects such as srub brush and remote control.
