  The main challenge in vision-and-language navigation (VLN) is how to
understand natural-language instructions in an unseen environment. The main
limitation of conventional VLN algorithms is that if an action is mistaken, the
agent fails to follow the instructions or explores unnecessary regions, leading
the agent to an irrecoverable path. To tackle this problem, we propose
Meta-Explore, a hierarchical navigation method deploying an exploitation policy
to correct misled recent actions. We show that an exploitation policy, which
moves the agent toward a well-chosen local goal among unvisited but observable
states, outperforms a method which moves the agent to a previously visited
state. We also highlight the demand for imagining regretful explorations with
semantically meaningful clues. The key to our approach is understanding the
object placements around the agent in spectral-domain. Specifically, we present
a novel visual representation, called scene object spectrum (SOS), which
performs category-wise 2D Fourier transform of detected objects. Combining
exploitation policy and SOS features, the agent can correct its path by
choosing a promising local goal. We evaluate our method in three VLN
benchmarks: R2R, SOON, and REVERIE. Meta-Explore outperforms other baselines
and shows significant generalization performance. In addition, local goal
search using the proposed spectral-domain SOS features significantly improves
the success rate by 17.1% and SPL by 20.6% for the SOON benchmark.
