  We develop a language-guided navigation task set in a continuous 3D
environment where agents must execute low-level actions to follow natural
language navigation directions. By being situated in continuous environments,
this setting lifts a number of assumptions implicit in prior work that
represents environments as a sparse graph of panoramas with edges corresponding
to navigability. Specifically, our setting drops the presumptions of known
environment topologies, short-range oracle navigation, and perfect agent
localization. To contextualize this new task, we develop models that mirror
many of the advances made in prior settings as well as single-modality
baselines. While some of these techniques transfer, we find significantly lower
absolute performance in the continuous setting -- suggesting that performance
in prior `navigation-graph' settings may be inflated by the strong implicit
assumptions.
