Integrating Disambiguation and User Preferences into Large Language Models for Robot Motion Planning
This work addresses the challenge of enhancing human-robot interaction for navigation tasks, though it is incremental as it builds on existing LLM and motion planning methods.
The paper tackles the problem of interpreting ambiguous natural language navigation commands for robots by integrating disambiguation and user preference capture into a framework using Large Language Models (LLMs), resulting in improved reliability and user experience as demonstrated in various test scenarios.
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.