  In the context of visual navigation, the capacity to map a novel environment
is necessary for an agent to exploit its observation history in the considered
place and efficiently reach known goals. This ability can be associated with
spatial reasoning, where an agent is able to perceive spatial relationships and
regularities, and discover object characteristics. Recent work introduces
learnable policies parametrized by deep neural networks and trained with
Reinforcement Learning (RL). In classical RL setups, the capacity to map and
reason spatially is learned end-to-end, from reward alone. In this setting, we
introduce supplementary supervision in the form of auxiliary tasks designed to
favor the emergence of spatial perception capabilities in agents trained for a
goal-reaching downstream objective. We show that learning to estimate metrics
quantifying the spatial relationships between an agent at a given location and
a goal to reach has a high positive impact in Multi-Object Navigation settings.
Our method significantly improves the performance of different baseline agents,
that either build an explicit or implicit representation of the environment,
even matching the performance of incomparable oracle agents taking ground-truth
maps as input. A learning-based agent from the literature trained with the
proposed auxiliary losses was the winning entry to the Multi-Object Navigation
Challenge, part of the CVPR 2021 Embodied AI Workshop.
