  We consider the problem of object goal navigation in unseen environments.
Solving this problem requires learning of contextual semantic priors, a
challenging endeavour given the spatial and semantic variability of indoor
environments. Current methods learn to implicitly encode these priors through
goal-oriented navigation policy functions operating on spatial representations
that are limited to the agent's observable areas. In this work, we propose a
novel framework that actively learns to generate semantic maps outside the
field of view of the agent and leverages the uncertainty over the semantic
classes in the unobserved areas to decide on long term goals. We demonstrate
that through this spatial prediction strategy, we are able to learn semantic
priors in scenes that can be leveraged in unknown environments. Additionally,
we show how different objectives can be defined by balancing exploration with
exploitation during searching for semantic targets. Our method is validated in
the visually realistic environments of the Matterport3D dataset and show
improved results on object goal navigation over competitive baselines.
