  This paper presents a framework that can interpret humans' navigation
commands containing temporal elements and directly translate their natural
language instructions into robot motion planning. Central to our framework is
utilizing Large Language Models (LLMs). To enhance the reliability of LLMs in
the framework and improve user experience, we propose methods to resolve the
ambiguity in natural language instructions and capture user preferences. The
process begins with an ambiguity classifier, identifying potential
uncertainties in the instructions. Ambiguous statements trigger a GPT-4-based
mechanism that generates clarifying questions, incorporating user responses for
disambiguation. Also, the framework assesses and records user preferences for
non-ambiguous instructions, enhancing future interactions. The last part of
this process is the translation of disambiguated instructions into a robot
motion plan using Linear Temporal Logic. This paper details the development of
this framework and the evaluation of its performance in various test scenarios.
