  We introduce a learning-based approach for room navigation using semantic
maps. Our proposed architecture learns to predict top-down belief maps of
regions that lie beyond the agent's field of view while modeling architectural
and stylistic regularities in houses. First, we train a model to generate
amodal semantic top-down maps indicating beliefs of location, size, and shape
of rooms by learning the underlying architectural patterns in houses. Next, we
use these maps to predict a point that lies in the target room and train a
policy to navigate to the point. We empirically demonstrate that by predicting
semantic maps, the model learns common correlations found in houses and
generalizes to novel environments. We also demonstrate that reducing the task
of room navigation to point navigation improves the performance further.
