Integrating Disambiguation and User Preferences into Large Language Models for Robot Motion Planning

TL;DR

This paper introduces a framework that uses Large Language Models to interpret natural language navigation commands, resolve ambiguities, incorporate user preferences, and generate robot motion plans with improved reliability and user experience.

Contribution

The paper presents a novel framework integrating disambiguation and user preferences into LLM-based robot motion planning, enhancing command interpretation accuracy.

Findings

Effective ambiguity detection and resolution in natural language commands.

Successful incorporation of user preferences for personalized planning.

Improved accuracy in translating instructions to robot motion plans.

Abstract

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.