A review of embodied intelligence systems: a three-layer framework integrating multimodal perception, world modeling, and structured strategies

Abstract

Embodied intelligent systems build upon the foundations of behavioral robotics and classical cognitive architectures. They integrate multimodal perception, world modeling, and adaptive control to support closed-loop interaction in dynamic and uncertain environments. Recent breakthroughs in Multimodal Large Models (MLMs) and World Models (WMs) are profoundly transforming this field, providing the tools to achieve its long-envisioned capabilities of semantic understanding and robust generalization. Targeting the central challenge of how modern MLMs and WMs jointly advance embodied intelligence, this review provides a comprehensive overview across key dimensions, including multimodal perception, cross-modal alignment, adaptive decision-making, and Sim-to-Real transfer. Furthermore, we systematize these components into a three-stage theoretical framework termed "Dynamic Perception-Task Adaptation (DP-TA)". This framework integrates multimodal perception modeling, causally driven world state prediction, and semantically guided strategy optimization, establishing a comprehensive "perception-modeling-decision" loop. To support this, we introduce a "Feature-Conditioned Modal Alignment (F-CMA)" mechanism to enhance cross-modal fusion under task constraints.