  Following a navigation instruction such as 'Walk down the stairs and stop at
the brown sofa' requires embodied AI agents to ground scene elements referenced
via language (e.g. 'stairs') to visual content in the environment (pixels
corresponding to 'stairs').
  We ask the following question -- can we leverage abundant 'disembodied'
web-scraped vision-and-language corpora (e.g. Conceptual Captions) to learn
visual groundings (what do 'stairs' look like?) that improve performance on a
relatively data-starved embodied perception task (Vision-and-Language
Navigation)? Specifically, we develop VLN-BERT, a visiolinguistic
transformer-based model for scoring the compatibility between an instruction
('...stop at the brown sofa') and a sequence of panoramic RGB images captured
by the agent. We demonstrate that pretraining VLN-BERT on image-text pairs from
the web before fine-tuning on embodied path-instruction data significantly
improves performance on VLN -- outperforming the prior state-of-the-art in the
fully-observed setting by 4 absolute percentage points on success rate.
Ablations of our pretraining curriculum show each stage to be impactful -- with
their combination resulting in further positive synergistic effects.
