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

Kordel France, Rohith Peddi, Nik Dennler, Ovidiu Daescu

Keywords: olfaction, computer vision, robotics, embodied artificial intelligence, natural language processing, signal processing, machine learning

TL;DR: A disproportionate amount of funding and research is given to artificial olfaction in comparison to vision, audio, and NLP which is inhibiting critical functions of artificial intelligence.

Abstract:

Despite extraordinary progress in artificial intelligence, 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 AI research. 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 human experience.

Submission Number: 566