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.

翻译：具身人工智能（Embodied AI）对于实现通用人工智能（AGI）至关重要，是连接虚拟空间与物理世界的各类应用的基础。近年来，多模态大模型（MLMs）与世界模型（WMs）因其卓越的感知、交互与推理能力而受到广泛关注，成为具身智能体“大脑”的潜在架构。然而，目前尚缺乏针对MLMs时代具身人工智能的全面综述。本综述系统性地探讨了具身人工智能的最新进展。我们首先梳理了具身机器人与仿真平台的前沿代表性工作，以全面理解其研究重点与现有局限。随后，我们分析了四个核心研究方向：1) 具身感知，2) 具身交互，3) 具身智能体，以及4) 仿真到真实环境的适应，涵盖了最新方法、关键范式与综合数据集。此外，本文深入探讨了多模态大模型在虚拟与真实具身智能体中的应用复杂性，强调了其在促进动态数字与物理环境交互方面的重要性。最后，我们总结了具身人工智能面临的挑战与局限，并讨论了其未来潜在发展方向。我们希望本综述能为研究界提供基础性参考，并激发持续创新。相关项目可在 https://github.com/HCPLab-SYSU/Embodied_AI_Paper_List 查看。