  Real-world navigation often involves dealing with unexpected obstructions
such as closed doors, moved objects, and unpredictable entities. However,
mainstream Vision-and-Language Navigation (VLN) tasks typically assume
instructions perfectly align with the fixed and predefined navigation graphs
without any obstructions. This assumption overlooks potential discrepancies in
actual navigation graphs and given instructions, which can cause major failures
for both indoor and outdoor agents. To address this issue, we integrate diverse
obstructions into the R2R dataset by modifying both the navigation graphs and
visual observations, introducing an innovative dataset and task, R2R with
UNexpected Obstructions (R2R-UNO). R2R-UNO contains various types and numbers
of path obstructions to generate instruction-reality mismatches for VLN
research. Experiments on R2R-UNO reveal that state-of-the-art VLN methods
inevitably encounter significant challenges when facing such mismatches,
indicating that they rigidly follow instructions rather than navigate
adaptively. Therefore, we propose a novel method called ObVLN (Obstructed VLN),
which includes a curriculum training strategy and virtual graph construction to
help agents effectively adapt to obstructed environments. Empirical results
show that ObVLN not only maintains robust performance in unobstructed scenarios
but also achieves a substantial performance advantage with unexpected
obstructions.
