  Vision-language navigation (VLN) is the task of entailing an agent to carry
out navigational instructions inside photo-realistic environments. One of the
key challenges in VLN is how to conduct a robust navigation by mitigating the
uncertainty caused by ambiguous instructions and insufficient observation of
the environment. Agents trained by current approaches typically suffer from
this and would consequently struggle to avoid random and inefficient actions at
every step. In contrast, when humans face such a challenge, they can still
maintain robust navigation by actively exploring the surroundings to gather
more information and thus make more confident navigation decisions. This work
draws inspiration from human navigation behavior and endows an agent with an
active information gathering ability for a more intelligent vision-language
navigation policy. To achieve this, we propose an end-to-end framework for
learning an exploration policy that decides i) when and where to explore, ii)
what information is worth gathering during exploration, and iii) how to adjust
the navigation decision after the exploration. The experimental results show
promising exploration strategies emerged from training, which leads to
significant boost in navigation performance. On the R2R challenge leaderboard,
our agent gets promising results all three VLN settings, i.e., single run,
pre-exploration, and beam search.
