  Recent studies in Vision-and-Language Navigation (VLN) train RL agents to
execute natural-language navigation instructions in photorealistic
environments, as a step towards robots that can follow human instructions.
However, given the scarcity of human instruction data and limited diversity in
the training environments, these agents still struggle with complex language
grounding and spatial language understanding. Pretraining on large text and
image-text datasets from the web has been extensively explored but the
improvements are limited. We investigate large-scale augmentation with
synthetic instructions. We take 500+ indoor environments captured in
densely-sampled 360 degree panoramas, construct navigation trajectories through
these panoramas, and generate a visually-grounded instruction for each
trajectory using Marky, a high-quality multilingual navigation instruction
generator. We also synthesize image observations from novel viewpoints using an
image-to-image GAN. The resulting dataset of 4.2M instruction-trajectory pairs
is two orders of magnitude larger than existing human-annotated datasets, and
contains a wider variety of environments and viewpoints. To efficiently
leverage data at this scale, we train a simple transformer agent with imitation
learning. On the challenging RxR dataset, our approach outperforms all existing
RL agents, improving the state-of-the-art NDTW from 71.1 to 79.1 in seen
environments, and from 64.6 to 66.8 in unseen test environments. Our work
points to a new path to improving instruction-following agents, emphasizing
large-scale imitation learning and the development of synthetic instruction
generation capabilities.
