  Embodied AI has made significant progress acting in unexplored environments.
However, tasks such as object search have largely focused on efficient policy
learning. In this work, we identify several gaps in current search methods:
They largely focus on dated perception models, neglect temporal aggregation,
and transfer from ground truth directly to noisy perception at test time,
without accounting for the resulting overconfidence in the perceived state. We
address the identified problems through calibrated perception probabilities and
uncertainty across aggregation and found decisions, thereby adapting the models
for sequential tasks. The resulting methods can be directly integrated with
pretrained models across a wide family of existing search approaches at no
additional training cost. We perform extensive evaluations of aggregation
methods across both different semantic perception models and policies,
confirming the importance of calibrated uncertainties in both the aggregation
and found decisions. We make the code and trained models available at
http://semantic-search.cs.uni-freiburg.de.
