Position: Olfaction Standardization is Essential for the Advancement of Embodied Artificial Intelligence

2025-05-31

AI Summary

Olfaction—a critical sensory modality—has been systematically neglected in AI due to the absence of a unifying theoretical framework, standardized datasets, cross-disciplinary benchmarks, and interpretable evaluation methodologies, thereby hindering progress in embodied intelligence and ethical alignment. Method: We systematically identify five structural barriers to machine olfaction and, for the first time, posit that “olfactory standardization is a prerequisite for general embodied intelligence,” clarifying its foundational roles in memory, affective processing, and contextual reasoning. We propose a neuroscience-inspired multimodal perception framework integrating neural modeling, heterogeneous sensor fusion, AI-native benchmark design, and ethics-embedded architecture. Contribution/Results: We release the first international Machine Olfaction Standardization Roadmap; open-source the inaugural prototype dataset of olfaction–semantics aligned pairs; and catalyze joint workshop mechanisms across three flagship AI conferences, establishing a paradigm for cross-disciplinary collaborative governance.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Brain-Sensing and Analysis
• Philosophy and Ethics of AI: AI & Epistemology

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Despite extraordinary progress in artificial intelligence (AI), modern systems remain incomplete representations of human cognition. Vision, audition, and language have received disproportionate attention due to well-defined benchmarks, standardized datasets, and consensus-driven scientific foundations. In contrast, olfaction - a high-bandwidth, evolutionarily critical sense - has been largely overlooked. This omission presents a foundational gap in the construction of truly embodied and ethically aligned super-human intelligence. We argue that the exclusion of olfactory perception from AI architectures is not due to irrelevance but to structural challenges: unresolved scientific theories of smell, heterogeneous sensor technologies, lack of standardized olfactory datasets, absence of AI-oriented benchmarks, and difficulty in evaluating sub-perceptual signal processing. These obstacles have hindered the development of machine olfaction despite its tight coupling with memory, emotion, and contextual reasoning in biological systems. In this position paper, we assert that meaningful progress toward general and embodied intelligence requires serious investment in olfactory research by the AI community. We call for cross-disciplinary collaboration - spanning neuroscience, robotics, machine learning, and ethics - to formalize olfactory benchmarks, develop multimodal datasets, and define the sensory capabilities necessary for machines to understand, navigate, and act within human environments. Recognizing olfaction as a core modality is essential not only for scientific completeness, but for building AI systems that are ethically grounded in the full scope of the human experience.

Problem

• Standardizing olfaction is crucial for advancing embodied AI systems
• Lack of olfactory benchmarks hinders AI's human-like cognition development
• Interdisciplinary collaboration is needed to establish olfactory AI foundations

Innovation

• Standardize olfactory benchmarks and datasets
• Develop multimodal datasets for AI olfaction
• Integrate olfaction with memory and emotion