Large language models for human–robot interaction: A review

Abstract

The fusion of large language models and robotic systems has introduced a transformative paradigm in human–robot interaction, offering unparalleled capabilities in natural language understanding and task execution. This review paper offers a comprehensive analysis of this nascent but rapidly evolving domain, spotlighting the recent advances of Large Language Models (LLMs) in enhancing their structures and performances, particularly in terms of multimodal input handling, high-level reasoning, and plan generation. Moreover, it probes the current methodologies that integrate LLMs into robotic systems for complex task completion, from traditional probabilistic models to the utilization of value functions and metrics for optimal decision-making. Despite these advancements, the paper also reveals the formidable challenges that confront the field, such as contextual understanding, data privacy and ethical considerations. To our best knowledge, this is the first study to comprehensively analyze the advances and considerations of LLMs in Human–Robot Interaction (HRI) based on recent progress, which provides potential avenues for further research.