LLM-Powered Embodied Intelligence for Socially-Aware Robot Navigation in Human-Robot Interaction.

Abstract

This doctoral research proposes a framework for developing sociallyaware robot navigation systems by integrating the cognitive capabilities of Large Language Models (LLMs) with the demands of real-world Human-Robot Interaction (HRI).Our work follows a four-stage plan that systematically addresses the challenges of applying LLMs to time-sensitive, safety-critical tasks.This paper details the completion of the first two stages, wherein we developed and evaluated a foundational navigation model.Our system features a meticulously designed multimodal fusion pipeline that integrates LiDAR and camera data, processed by a YOLO model and a Hungarian algorithm for semantic association, providing rich, contextual input to the LLM.Through knowledge distillation and fine-tuning on data from a custom simulator, our model demonstrates robust spatial reasoning and superior performance in low-frequency decision-making scenarios compared to traditional reinforcement learning methods.We successfully validated this foundational model and identified its inference latency as a key challenge.These results establish a solid basis for our future work.This includes developing a "brain-cerebellum" hybrid architecture for real-time performance and exploring multi-robot social compliance.This research contributes to HRI by creating more predictable and trustworthy robots, and to the LLM field by investigating the symbol grounding problem through embodied intelligence.