  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.
