  Capitalizing on the remarkable advancements in Large Language Models (LLMs),
there is a burgeoning initiative to harness LLMs for instruction following
robotic navigation. Such a trend underscores the potential of LLMs to
generalize navigational reasoning and diverse language understanding. However,
a significant discrepancy in agent performance is observed when integrating
LLMs in the Vision-and-Language navigation (VLN) tasks compared to previous
downstream specialist models. Furthermore, the inherent capacity of language to
interpret and facilitate communication in agent interactions is often
underutilized in these integrations. In this work, we strive to bridge the
divide between VLN-specialized models and LLM-based navigation paradigms, while
maintaining the interpretative prowess of LLMs in generating linguistic
navigational reasoning. By aligning visual content in a frozen LLM, we
encompass visual observation comprehension for LLMs and exploit a way to
incorporate LLMs and navigation policy networks for effective action
predictions and navigational reasoning. We demonstrate the data efficiency of
the proposed methods and eliminate the gap between LM-based agents and
state-of-the-art VLN specialists.
