Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI
<|reference_start|>Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI: 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.<|reference_end|>
@article{liu2024aligning,
    title={Aligning Cyber Space with Physical World: A Comprehensive Survey on
  Embodied AI},
    author={Yang Liu, Weixing Chen, Yongjie Bai, Xiaodan Liang, Guanbin Li, Wen
  Gao, Liang Lin},
    journal={arXiv preprint arXiv:2407.06886},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06886},
    primaryClass={cs.CV cs.AI cs.LG cs.MA cs.RO}
}