  The realization of universal robots is an ultimate goal of researchers.
However, a key hurdle in achieving this goal lies in the robots' ability to
manipulate objects in their unstructured surrounding environments according to
different tasks. The learning-based approach is considered an effective way to
address generalization. The impressive performance of foundation models in the
fields of computer vision and natural language suggests the potential of
embedding foundation models into manipulation tasks as a viable path toward
achieving general manipulation capability. However, we believe achieving
general manipulation capability requires an overarching framework akin to auto
driving. This framework should encompass multiple functional modules, with
different foundation models assuming distinct roles in facilitating general
manipulation capability. This survey focuses on the contributions of foundation
models to robot learning for manipulation. We propose a comprehensive framework
and detail how foundation models can address challenges in each module of the
framework. What's more, we examine current approaches, outline challenges,
suggest future research directions, and identify potential risks associated
with integrating foundation models into this domain.
