  We consider the problems of exploration and point-goal navigation in
previously unseen environments, where the spatial complexity of indoor scenes
and partial observability constitute these tasks challenging. We argue that
learning occupancy priors over indoor maps provides significant advantages
towards addressing these problems. To this end, we present a novel planning
framework that first learns to generate occupancy maps beyond the field-of-view
of the agent, and second leverages the model uncertainty over the generated
areas to formulate path selection policies for each task of interest. For
point-goal navigation the policy chooses paths with an upper confidence bound
policy for efficient and traversable paths, while for exploration the policy
maximizes model uncertainty over candidate paths. We perform experiments in the
visually realistic environments of Matterport3D using the Habitat simulator and
demonstrate: 1) Improved results on exploration and map quality metrics over
competitive methods, and 2) The effectiveness of our planning module when
paired with the state-of-the-art DD-PPO method for the point-goal navigation
task.
