  Recently, numerous algorithms have been developed to tackle the problem of
vision-language navigation (VLN), i.e., entailing an agent to navigate 3D
environments through following linguistic instructions. However, current VLN
agents simply store their past experiences/observations as latent states in
recurrent networks, failing to capture environment layouts and make long-term
planning. To address these limitations, we propose a crucial architecture,
called Structured Scene Memory (SSM). It is compartmentalized enough to
accurately memorize the percepts during navigation. It also serves as a
structured scene representation, which captures and disentangles visual and
geometric cues in the environment. SSM has a collect-read controller that
adaptively collects information for supporting current decision making and
mimics iterative algorithms for long-range reasoning. As SSM provides a
complete action space, i.e., all the navigable places on the map, a
frontier-exploration based navigation decision making strategy is introduced to
enable efficient and global planning. Experiment results on two VLN datasets
(i.e., R2R and R4R) show that our method achieves state-of-the-art performance
on several metrics.
