Embodied Artificial General Intelligence Architectures: an Integrative Approach to Cognitive, Sensorimotor, and Environmental Interaction Systems

Abstract

Embodied Artificial General Intelligence (AGI) represents a transformative paradigm that integrates cognitive processing, sensorimotor systems, environmental adaptability, and self-learning mechanisms into a unified architecture. Unlike traditional AI, which operates in virtual or constrained domains, embodied AGI is designed to understand, act, and evolve in the physical world through continuous perception and interaction. This paper presents an in-depth exploration of embodied AGI architectures, emphasizing their structural design, cognitive models, neural-simulation interfaces, and the principles of embodied cognition. It discusses recent advancements in robotics, neuromorphic computing, and hybrid symbolic–connectionist systems that contribute to the evolution of AGI. Furthermore, the study outlines key challenges, limitations, and prospective research avenues to achieve robust general intelligence that seamlessly integrates body, mind, and environment.

KEYWORDS: Embodied AGI, Cognitive Architecture, Sensorimotor Integration, Neuromorphic Computing, Symbolic-Connectionist Systems, Machine Learning, Robotics, Artificial Consciousness