  While the exploration for embodied AI has spanned multiple decades, it
remains a persistent challenge to endow agents with human-level intelligence,
including perception, learning, reasoning, decision-making, control, and
generalization capabilities, so that they can perform general-purpose tasks in
open, unstructured, and dynamic environments. Recent advances in computer
vision, natural language processing, and multi-modality learning have shown
that the foundation models have superhuman capabilities for specific tasks.
They not only provide a solid cornerstone for integrating basic modules into
embodied AI systems but also shed light on how to scale up robot learning from
a methodological perspective. This survey aims to provide a comprehensive and
up-to-date overview of foundation models in robotics, focusing on autonomous
manipulation and encompassing high-level planning and low-level control.
Moreover, we showcase their commonly used datasets, simulators, and benchmarks.
Importantly, we emphasize the critical challenges intrinsic to this field and
delineate potential avenues for future research, contributing to advancing the
frontier of academic and industrial discourse.
