  Embodied Artificial Intelligence (Embodied AI) is crucial for achieving
Artificial General Intelligence (AGI) and serves as a foundation for various
applications that bridge cyberspace and the physical world. Recently, the
emergence of Multi-modal Large Models (MLMs) and World Models (WMs) have
attracted significant attention due to their remarkable perception,
interaction, and reasoning capabilities, making them a promising architecture
for the brain of embodied agents. However, there is no comprehensive survey for
Embodied AI in the era of MLMs. In this survey, we give a comprehensive
exploration of the latest advancements in Embodied AI. Our analysis firstly
navigates through the forefront of representative works of embodied robots and
simulators, to fully understand the research focuses and their limitations.
Then, we analyze four main research targets: 1) embodied perception, 2)
embodied interaction, 3) embodied agent, and 4) sim-to-real adaptation,
covering the state-of-the-art methods, essential paradigms, and comprehensive
datasets. Additionally, we explore the complexities of MLMs in virtual and real
embodied agents, highlighting their significance in facilitating interactions
in dynamic digital and physical environments. Finally, we summarize the
challenges and limitations of embodied AI and discuss their potential future
directions. We hope this survey will serve as a foundational reference for the
research community and inspire continued innovation. The associated project can
be found at https://github.com/HCPLab-SYSU/Embodied_AI_Paper_List.
