Large language model-based task planning for service robots: A review

Abstract

With the rapid advancement of large language models (LLMs) and robotics, service robots are increasingly becoming an integral part of daily life, offering a wide range of services in complex environments. To deliver these services intelligently and efficiently, robust and accurate task planning capabilities are essential. This paper presents a comprehensive overview of the integration of LLMs into service robotics, with a particular focus on their role in enhancing robotic task planning. First, the development and foundational techniques of LLMs, including pre-training, fine-tuning, retrieval-augmented generation (RAG), and prompt engineering, are reviewed. We then explore the application of LLMs as the cognitive core—“brain”—of service robots, discussing how LLMs contribute to improved autonomy and decision-making. Furthermore, recent advancements in LLM-driven task planning across various input modalities are analyzed, including text, visual, audio, and multimodal inputs. Finally, we summarize key challenges and limitations in current research and propose future directions to advance the task planning capabilities of service robots in complex, unstructured domestic environments. This review aims to serve as a valuable reference for researchers and practitioners in the fields of artificial intelligence and robotics.

Keywords

Large language model / Service robot / Task planning / Review

CRediT authorship contribution statement

Shaohan Bian: Writing – review & editing, Writing – original draft, Investigation, Formal analysis. Ying Zhang: Writing – review & editing, Writing – original draft, Supervision, Investigation. Guohui Tian: Writing – review & editing, Investigation. Zhiqiang Miao: Writing – review & editing, Writing – original draft, Investigation. Edmond Q. Wu: Writing – review & editing, Investigation. Simon X. Yang: Writing – review & editing, Investigation. Changchun Hua: Writing – review & editing, Supervision, Investigation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (62203378 and 62203377), in part by the Hebei Natural Science Foundation (F2024203036), in part by the Beijing-Tianjin-Hebei Basic Research Cooperation Project of Hebei Natural Science Foundation (F2024203115), in part by the Science Research Project of Hebei Education Department (BJK2024195), and in part by the S&T Program of Hebei (236Z2002G and 236Z1603G).

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