Intent-Driven Embodied Artificial Intelligence

Abstract

Embodied Artificial Intelligence (Embodied AI) has emerged as a promising paradigm for developing more general and adaptive intelligent systems, emphasizing that intelligence emerges from continuous interaction among perception, cognition, and action in real-world environments. Recent advances increasingly integrate large language models and multimodal learning into embodied agents; however, most existing approaches remain correlation-driven, relying on implicit objectives, task-specific rewards, or prompt-level instructions. As a consequence, intent is rarely represented explicitly, limiting causal coherence, long-horizon consistency, and robust value alignment in open-world settings. In this Review, we synthesize recent progress in Embodied AI and articulate Intent-Driven Embodied Artificial Intelligence (IDEAI) as a system-level organizing framework in which intent functions as an explicit, revisable, and verifiable mediating construct between human goals, environmental constraints, and agent behavior. Building on this synthesis, we propose a four-layer conceptual organization-semantic grounding, concept generation and learning, intent modeling, and value alignment-that clarifies how explicit intent mediates perception, cognition, and action in embodied systems. We analyze how existing techniques address recurring failure modes along the intent-to-execution pipeline and highlight the limitations that arise when intent remains implicit. By making intent explicit, revisable, and value-constrained where such structure is needed, IDEAI supports interpretable decision-making, adaptive task decomposition, and value-consistent behavior in open-ended, human-interactive, and safety-critical embodied domains, providing a unifying perspective for advancing Embodied AI toward robust, socially deployable intelligent systems.