  Vision-and-language navigation (VLN) is a challenging task that requires an
agent to navigate in real-world environments by understanding natural language
instructions and visual information received in real-time. Prior works have
implemented VLN tasks on continuous environments or physical robots, all of
which use a fixed camera configuration due to the limitations of datasets, such
as 1.5 meters height, 90 degrees horizontal field of view (HFOV), etc. However,
real-life robots with different purposes have multiple camera configurations,
and the huge gap in visual information makes it difficult to directly transfer
the learned navigation model between various robots. In this paper, we propose
a visual perception generalization strategy based on meta-learning, which
enables the agent to fast adapt to a new camera configuration with a few shots.
In the training phase, we first locate the generalization problem to the visual
perception module, and then compare two meta-learning algorithms for better
generalization in seen and unseen environments. One of them uses the
Model-Agnostic Meta-Learning (MAML) algorithm that requires a few shot
adaptation, and the other refers to a metric-based meta-learning method with a
feature-wise affine transformation layer. The experiment results show that our
strategy successfully adapts the learned navigation model to a new camera
configuration, and the two algorithms show their advantages in seen and unseen
environments respectively.
