  Recent research in language-guided visual navigation has demonstrated a
significant demand for the diversity of traversable environments and the
quantity of supervision for training generalizable agents. To tackle the common
data scarcity issue in existing vision-and-language navigation datasets, we
propose an effective paradigm for generating large-scale data for learning,
which applies 1200+ photo-realistic environments from HM3D and Gibson datasets
and synthesizes 4.9 million instruction trajectory pairs using fully-accessible
resources on the web. Importantly, we investigate the influence of each
component in this paradigm on the agent's performance and study how to
adequately apply the augmented data to pre-train and fine-tune an agent. Thanks
to our large-scale dataset, the performance of an existing agent can be pushed
up (+11% absolute with regard to previous SoTA) to a significantly new best of
80% single-run success rate on the R2R test split by simple imitation learning.
The long-lasting generalization gap between navigating in seen and unseen
environments is also reduced to less than 1% (versus 8% in the previous best
method). Moreover, our paradigm also facilitates different models to achieve
new state-of-the-art navigation results on CVDN, REVERIE, and R2R in continuous
environments.
