The research on pre-trained large models has made remarkable achievements in recent years, this paper reviews the application of pre-trained large model in robotics. Traditional deep learning models in robots were trained on small datasets customized for specific tasks, which limits their adaptability in different applications. In contrast, large models pre-trained on internet-scale data appear to have superior generalization capabilities and in some cases show an exploratory ability to find one-shot solutions where they are not present in the training data. The underlying model has the potential to enhance the various components of a robot’s autonomous task, from perception to decision making and control. This paper examines recent papers that use or build large models to solve robotics problems, exploring how large models can help improve robots’ capabilities in the areas of perception, decision making, and control, thereby promoting the application of large robot models in more fields. Meanwhile, the challenges that hinder the application of large models in robotic autonomous systems were discussed in this paper, such as data scarcity in robotic applications, the variability of robots themselves, the limitations of multimodal representations, and real-time performance, and provides opportunities and potential approaches for future improvements.