This survey provides a comprehensive overview of the application and advancements of Neural Radiance Fields (NeRF) in robotics. NeRF, a neural implicit representation, has gained significant attention due to its simplified mathematical models, compact environment storage, and continuous scene representations. The survey is divided into two main sections: *The Application of NeRF in Robotics* and *The Advance of NeRF in Robotics*. In the first section, the authors explore how NeRF can be utilized in robotics from both perception and interaction perspectives. They discuss various applications such as scene reconstruction, dynamic scene reconstruction, scene segmentation, scene editing, and robotic navigation and manipulation. For example, NeRF is used for indoor and outdoor scene reconstruction, object segmentation, and semantic segmentation, as well as for generating realistic 3D scenes for robotic navigation and manipulation tasks. The second section focuses on enhancing NeRF's properties to improve its performance in robotics. This includes techniques such as positional encoding, hierarchical volume rendering, and the integration of explicit models to address limitations in geometry editing. The authors also discuss the challenges and future directions in the field, emphasizing the need for more efficient and accurate methods for robotic applications. Overall, the survey highlights the potential of NeRF in robotics and provides valuable insights into its current and potential future applications.This survey provides a comprehensive overview of the application and advancements of Neural Radiance Fields (NeRF) in robotics. NeRF, a neural implicit representation, has gained significant attention due to its simplified mathematical models, compact environment storage, and continuous scene representations. The survey is divided into two main sections: *The Application of NeRF in Robotics* and *The Advance of NeRF in Robotics*. In the first section, the authors explore how NeRF can be utilized in robotics from both perception and interaction perspectives. They discuss various applications such as scene reconstruction, dynamic scene reconstruction, scene segmentation, scene editing, and robotic navigation and manipulation. For example, NeRF is used for indoor and outdoor scene reconstruction, object segmentation, and semantic segmentation, as well as for generating realistic 3D scenes for robotic navigation and manipulation tasks. The second section focuses on enhancing NeRF's properties to improve its performance in robotics. This includes techniques such as positional encoding, hierarchical volume rendering, and the integration of explicit models to address limitations in geometry editing. The authors also discuss the challenges and future directions in the field, emphasizing the need for more efficient and accurate methods for robotic applications. Overall, the survey highlights the potential of NeRF in robotics and provides valuable insights into its current and potential future applications.