  Vision and language navigation (VLN) is a challenging visually-grounded
language understanding task. Given a natural language navigation instruction, a
visual agent interacts with a graph-based environment equipped with panorama
images and tries to follow the described route. Most prior work has been
conducted in indoor scenarios where best results were obtained for navigation
on routes that are similar to the training routes, with sharp drops in
performance when testing on unseen environments. We focus on VLN in outdoor
scenarios and find that in contrast to indoor VLN, most of the gain in outdoor
VLN on unseen data is due to features like junction type embedding or heading
delta that are specific to the respective environment graph, while image
information plays a very minor role in generalizing VLN to unseen outdoor
areas. These findings show a bias to specifics of graph representations of
urban environments, demanding that VLN tasks grow in scale and diversity of
geographical environments.
