The human ability to learn, generalize, and control complex manipulation tasks through multi-modality feedback suggests a unique capability, which we refer to as dexterity intelligence. Understanding and assessing this intelligence is a complex task. Amidst the swift progress and extensive proliferation of large language models (LLMs), their applications in the field of robotics have garnered increasing attention. LLMs possess the ability to process and generate natural language, facilitating efficient interaction and collaboration with robots. Researchers and engineers in the field of robotics have recognized the immense potential of LLMs in enhancing robot intelligence, human-robot interaction, and autonomy. Therefore, this comprehensive review aims to summarize the applications of LLMs in robotics, delving into their impact and contributions to key areas such as robot control, perception, decision-making, and path planning. We first provide an overview of the background and development of LLMs for robotics, followed by a description of the benefits of LLMs for robotics and recent advancements in robotics models based on LLMs. We then delve into the various techniques used in the model, including those employed in perception, decision-making, control, and interaction. Finally, we explore the applications of LLMs in robotics and some potential challenges they may face in the near future. Embodied intelligence is the future of intelligent science, and LLMs-based robotics is one of the promising but challenging paths to achieve this.

翻译：人类通过多模态反馈学习、泛化并控制复杂操作任务的能力，体现了一种独特的能力，我们称之为灵巧智能。理解和评估这种智能是一项复杂的任务。随着大型语言模型（LLMs）的快速发展和广泛推广，其在机器人领域的应用日益受到关注。LLMs具有处理和生成自然语言的能力，能够促进与机器人的高效交互与协作。机器人领域的研究人员和工程师已认识到LLMs在提升机器人智能、人机交互及自主性方面的巨大潜力。因此，本综述旨在系统总结LLMs在机器人领域的应用，深入探讨它们在机器人控制、感知、决策制定及路径规划等关键领域的影响与贡献。我们首先概述了LLMs在机器人领域应用的背景与发展，随后阐述了LLMs对机器人技术的优势以及基于LLMs的机器人模型的最新进展。接着，我们深入分析了模型所采用的各种技术，包括感知、决策、控制和交互中的技术。最后，我们探讨了LLMs在机器人领域的应用及其在近期可能面临的潜在挑战。具身智能是智能科学的未来方向，而基于LLMs的机器人技术是实现这一目标的一条充满希望但充满挑战的路径。