Intelligent Indoor Navigation for Home Robots based on Large Language Models

Abstract

Natural language understanding is crucial for home robots to help people in their daily lives. However, existing home robots mainly rely on keyword matching to understand explicit commands, while struggling with understanding human instructions expressed more implicitly and naturally. Recently, large language models (LLMs) have demonstrated great potential in human language understanding. In this paper, we explore the application of LLM in home robots with a focus on navigation, one of the core capabilities in mobile robots. First, we designed and implemented an LLM-assisted robot navigation framework which adopts a modular architecture to integrate human-robot interaction, semantic mapping, and motion planning, thereby enhancing scalability and deployment flexibility. Second, we established a systematic method and benchmarks to evaluate the performance of LLMs in robot navigation applications, quantifying the performance differences between cloud-based and local LLM models. Finally, we conducted experiments on a custom-designed mobile robot in a real apartment setting. Our findings provide practical insight in selecting and optimizing LLMs for robotic applications.