  The proliferation of Large Language Models (LLMs) has s fueled a shift in
robot learning from automation towards general embodied Artificial Intelligence
(AI). Adopting foundation models together with traditional learning methods to
robot learning has increasingly gained recent interest research community and
showed potential for real-life application. However, there are few literatures
comprehensively reviewing the relatively new technologies combined with
robotics. The purpose of this review is to systematically assess the
state-of-the-art foundation model techniques in the robot learning and to
identify future potential areas. Specifically, we first summarized the
technical evolution of robot learning and identified the necessary preliminary
preparations for foundation models including the simulators, datasets,
foundation model framework. In addition, we focused on the following four
mainstream areas of robot learning including manipulation, navigation,
planning, and reasoning and demonstrated how the foundation model techniques
can be adopted in the above scenarios. Furthermore, critical issues which are
neglected in the current literatures including robot hardware and software
decoupling, dynamic data, generalization performance with the presence of
human, etc. were discussed. This review highlights the state-of-the-art
progress of foundation models in robot learning and future research should
focus on multimodal interaction especially dynamics data, exclusive foundation
models for robots, and AI alignment, etc.
