  Learning to navigate in a visual environment following natural-language
instructions is a challenging task, because the multimodal inputs to the agent
are highly variable, and the training data on a new task is often limited. In
this paper, we present the first pre-training and fine-tuning paradigm for
vision-and-language navigation (VLN) tasks. By training on a large amount of
image-text-action triplets in a self-supervised learning manner, the
pre-trained model provides generic representations of visual environments and
language instructions. It can be easily used as a drop-in for existing VLN
frameworks, leading to the proposed agent called Prevalent. It learns more
effectively in new tasks and generalizes better in a previously unseen
environment. The performance is validated on three VLN tasks. On the
Room-to-Room benchmark, our model improves the state-of-the-art from 47% to 51%
on success rate weighted by path length. Further, the learned representation is
transferable to other VLN tasks. On two recent tasks, vision-and-dialog
navigation and "Help, Anna!" the proposed Prevalent leads to significant
improvement over existing methods, achieving a new state of the art.
