  Humans have the remarkable ability to navigate through unfamiliar
environments by solely relying on our prior knowledge and descriptions of the
environment. For robots to perform the same type of navigation, they need to be
able to associate natural language descriptions with their associated physical
environment with a limited amount of prior knowledge. Recently, Large Language
Models (LLMs) have been able to reason over billions of parameters and utilize
them in multi-modal chat-based natural language responses. However, LLMs lack
real-world awareness and their outputs are not always predictable. In this
work, we develop NavCon, a low-bandwidth framework that solves this lack of
real-world generalization by creating an intermediate layer between an LLM and
a robot navigation framework in the form of Python code. Our intermediate
shoehorns the vast prior knowledge inherent in an LLM model into a series of
input and output API instructions that a mobile robot can understand. We
evaluate our method across four different environments and command classes on a
mobile robot and highlight our NavCon's ability to interpret contextual
commands.
