  In the Vision-and-Language Navigation (VLN) task, an agent with egocentric
vision navigates to a destination given natural language instructions. The act
of manually annotating these instructions is timely and expensive, such that
many existing approaches automatically generate additional samples to improve
agent performance. However, these approaches still have difficulty generalizing
their performance to new environments. In this work, we investigate the popular
Room-to-Room (R2R) VLN benchmark and discover that what is important is not
only the amount of data you synthesize, but also how you do it. We find that
shortest path sampling, which is used by both the R2R benchmark and existing
augmentation methods, encode biases in the action space of the agent which we
dub as action priors. We then show that these action priors offer one
explanation toward the poor generalization of existing works. To mitigate such
priors, we propose a path sampling method based on random walks to augment the
data. By training with this augmentation strategy, our agent is able to
generalize better to unknown environments compared to the baseline,
significantly improving model performance in the process.
