  Object goal navigation aims to steer an agent towards a target object based
on observations of the agent. It is of pivotal importance to design effective
visual representations of the observed scene in determining navigation actions.
In this paper, we introduce a Visual Transformer Network (VTNet) for learning
informative visual representation in navigation. VTNet is a highly effective
structure that embodies two key properties for visual representations: First,
the relationships among all the object instances in a scene are exploited;
Second, the spatial locations of objects and image regions are emphasized so
that directional navigation signals can be learned. Furthermore, we also
develop a pre-training scheme to associate the visual representations with
navigation signals, and thus facilitate navigation policy learning. In a
nutshell, VTNet embeds object and region features with their location cues as
spatial-aware descriptors and then incorporates all the encoded descriptors
through attention operations to achieve informative representation for
navigation. Given such visual representations, agents are able to explore the
correlations between visual observations and navigation actions. For example,
an agent would prioritize "turning right" over "turning left" when the visual
representation emphasizes on the right side of activation map. Experiments in
the artificial environment AI2-Thor demonstrate that VTNet significantly
outperforms state-of-the-art methods in unseen testing environments.
