Selective attention or inattention? Enabling efficient perception in embodied intelligence agents

Abstract As embodied intelligence increasingly operates in the physical world, perception efficiency, robustness, and adaptability become central bottlenecks separating artificial agents from biological organisms. Unlike conventional perception pipelines, biological perception is inherently selective and resource-aware: attention amplifies task-relevant information, while inattention suppresses distraction and redundancy. This survey investigates attention-driven adaptive perception as a promising mechanism for improving embodied systems. We consolidate interdisciplinary findings from neuroscience, cognitive science, robotics, and machine learning to clarify the distinct yet complementary roles of selective attention and inattention in optimizing perceptual processing. Building on these insights, we introduce the Unified Selective Attention and Inattention Model (U-SAIM) and extend it into a three-tier architectural framework that maps biological principles to embodied perception systems. Guided by this structure, we review existing implementations across robotics, multimodal fusion, lightweight perception, and attention modulation strategies, extracting design patterns and performance implications. Moreover, we discuss the challenges and opportunities for future development. Together, these perspectives chart a unified theoretical and practical roadmap toward attentional embodied perception—agents capable of perceiving, reasoning, and acting with the adaptive intelligence, efficiency, and flexibility that define human perception.