  Vision-language Navigation (VLN) tasks require an agent to navigate
step-by-step while perceiving the visual observations and comprehending a
natural language instruction. Large data bias, which is caused by the disparity
ratio between the small data scale and large navigation space, makes the VLN
task challenging. Previous works have proposed various data augmentation
methods to reduce data bias. However, these works do not explicitly reduce the
data bias across different house scenes. Therefore, the agent would overfit to
the seen scenes and achieve poor navigation performance in the unseen scenes.
To tackle this problem, we propose the Random Environmental Mixup (REM) method,
which generates cross-connected house scenes as augmented data via mixuping
environment. Specifically, we first select key viewpoints according to the room
connection graph for each scene. Then, we cross-connect the key views of
different scenes to construct augmented scenes. Finally, we generate augmented
instruction-path pairs in the cross-connected scenes. The experimental results
on benchmark datasets demonstrate that our augmentation data via REM help the
agent reduce its performance gap between the seen and unseen environment and
improve the overall performance, making our model the best existing approach on
the standard VLN benchmark. The code have released:
https://github.com/LCFractal/VLNREM.
