We propose Embodied AI as the next fundamental step in the pursuit of
Artificial General Intelligence, juxtaposing it against current AI
advancements, particularly Large Language Models. We traverse the evolution of
the embodiment concept across diverse fields - philosophy, psychology,
neuroscience, and robotics - to highlight how EAI distinguishes itself from the
classical paradigm of static learning. By broadening the scope of Embodied AI,
we introduce a theoretical framework based on cognitive architectures,
emphasizing perception, action, memory, and learning as essential components of
an embodied agent. This framework is aligned with Friston's active inference
principle, offering a comprehensive approach to EAI development. Despite the
progress made in the field of AI, substantial challenges, such as the
formulation of a novel AI learning theory and the innovation of advanced
hardware, persist. Our discussion lays down a foundational guideline for future
Embodied AI research. Highlighting the importance of creating Embodied AI
agents capable of seamless communication, collaboration, and coexistence with
humans and other intelligent entities within real-world environments, we aim to
steer the AI community towards addressing the multifaceted challenges and
seizing the opportunities that lie ahead in the quest for AGI.

我们提出了 “具身人工智能” 作为追求 “人工通用智能” 的下一个基本步骤，并对其与当前人工智能进展，特别是大型语言模型进行对比。我们横跨哲学、心理学、神经科学和机器人技术等多个领域探讨了具身概念的演变，以突显具身人工智能如何与静态学习的经典范式有所区别。通过拓宽具身人工智能的范围，我们提出了一个基于认知架构的理论框架，强调感知、行动、记忆和学习作为具身代理的重要组成部分。该框架与弗里斯顿的主动推理原则相一致，为具身人工智能的发展提供了一个全面的方法。尽管人工智能领域取得了进展，但仍存在诸多挑战，如制定新的人工智能学习理论和创新先进硬件等。我们的讨论为未来的具身人工智能研究提供了基础性的指导方针。强调创造具身人工智能代理能够与人类和其他智能体在真实环境中实现无缝通信、协作和共存的重要性，我们旨在引导人工智能社群应对多方面挑战，并抓住在追求人工通用智能过程中出现的机遇。