Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields. The recent emergence of neural implicit representations has introduced radical innovation to this field as implicit representations enable numerous capabilities. Among these, the Neural Radiance Field (NeRF) has sparked a trend because of the huge representational advantages, such as simplified mathematical models, compact environment storage, and continuous scene representations. Apart from computer vision, NeRF has also shown tremendous potential in the field of robotics. Thus, we create this survey to provide a comprehensive understanding of NeRF in the field of robotics. By exploring the advantages and limitations of NeRF, as well as its current applications and future potential, we hope to shed light on this promising area of research. Our survey is divided into two main sections: \textit{The Application of NeRF in Robotics} and \textit{The Advance of NeRF in Robotics}, from the perspective of how NeRF enters the field of robotics. In the first section, we introduce and analyze some works that have been or could be used in the field of robotics from the perception and interaction perspectives. In the second section, we show some works related to improving NeRF's own properties, which are essential for deploying NeRF in the field of robotics. In the discussion section of the review, we summarize the existing challenges and provide some valuable future research directions for reference.

翻译：精细的三维环境表示一直是计算机视觉和机器人领域的长期目标。近年来，神经隐式表示的出现为该领域带来了革命性创新，因为隐式表示能够实现众多功能。其中，神经辐射场（NeRF）因其巨大的表示优势——如简化的数学模型、紧凑的环境存储以及连续的场景表示——而引发了一股研究热潮。除了计算机视觉，NeRF在机器人领域也展现出巨大的应用潜力。因此，我们撰写本篇综述，旨在全面理解NeRF在机器人领域的研究现状。通过探讨NeRF的优势与局限，以及其当前应用与未来潜力，我们希望为这一前景广阔的研究领域提供启示。本综述主要分为两个部分：*机器人领域的NeRF应用*与*机器人领域的NeRF进展*，从NeRF如何进入机器人领域的视角展开论述。在第一部分中，我们从感知与交互的角度介绍并分析了部分已应用于或可应用于机器人领域的研究工作。在第二部分中，我们展示了与改进NeRF自身特性相关的研究成果，这些特性对NeRF在机器人领域的部署至关重要。在综述的讨论章节中，我们总结了现有挑战，并提出了若干有价值的未来研究方向以供参考。