  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.
