Real-World Robot Applications of Foundation Models: A Review
Kento Kawaharazuka Tatsuya Matsushima Andrew Gambardella Jiaxian Guo Chris Paxton Andy Zeng
Recent developments in foundation models, like Large Language Models (LLMs)
and Vision-Language Models (VLMs), trained on extensive data, facilitate
flexible application across different tasks and modalities. Their impact spans
various fields, including healthcare, education, and robotics. This paper
provides an overview of the practical application of foundation models in
real-world robotics, with a primary emphasis on the replacement of specific
components within existing robot systems. The summary encompasses the
perspective of input-output relationships in foundation models, as well as
their role in perception, motion planning, and control within the field of
robotics. This paper concludes with a discussion of future challenges and
implications for practical robot applications.