Position: Olfaction Standardization is Essential for the Advancement of Embodied Artificial Intelligence
作者: Kordel K. France, Rohith Peddi, Nik Dennler, Ovidiu Daescu
分类: cs.AI, cs.RO
发布日期: 2025-05-31
💡 一句话要点
呼吁标准化嗅觉研究以推动具身人工智能发展
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 嗅觉感知 具身人工智能 跨学科合作 数据集标准化 情感计算
📋 核心要点
- 核心问题：现有的AI系统在嗅觉感知方面存在显著缺失，导致其无法全面模拟人类认知。
- 方法要点：论文主张通过跨学科合作，推动嗅觉研究的标准化，以促进具身人工智能的发展。
- 实验或效果：呼吁建立嗅觉基准和多模态数据集，以提升机器在复杂环境中的理解和适应能力。
📝 摘要（中文）
尽管人工智能（AI）取得了显著进展，但现代系统仍未能完整地代表人类认知。视觉、听觉和语言因其明确的基准和标准化数据集而受到过度关注，而嗅觉这一重要感官却被忽视。本文指出，嗅觉在AI架构中的缺失并非无关紧要，而是由于科学理论、传感器技术、数据集标准化等结构性挑战。为实现真正的具身智能，AI社区需加大对嗅觉研究的投资，促进跨学科合作，以建立嗅觉基准和多模态数据集，从而使机器能够理解和适应人类环境。
🔬 方法详解
问题定义：本文旨在解决嗅觉在人工智能架构中的缺失问题，指出现有方法在嗅觉感知方面的不足，导致AI无法全面模拟人类的认知能力。
核心思路：论文提出，嗅觉作为一种重要的感知方式，必须被纳入AI研究的核心范畴。通过标准化嗅觉研究，AI系统能够更好地理解和适应人类环境。
技术框架：整体架构包括嗅觉感知模块、数据集标准化模块和跨学科合作平台。嗅觉感知模块负责收集和处理嗅觉数据，数据集标准化模块则致力于建立统一的嗅觉数据集，合作平台促进各领域专家的交流与合作。
关键创新：最重要的创新在于将嗅觉视为AI系统的核心感知能力，强调其在情感、记忆和上下文推理中的重要性，这与现有方法的单一感知模式形成鲜明对比。
关键设计：在设计中，需关注嗅觉传感器的选择、数据集的构建标准、以及如何评估机器对嗅觉信号的处理能力等技术细节。
📊 实验亮点
论文强调了嗅觉在AI系统中的重要性，呼吁建立标准化的嗅觉数据集和基准。通过跨学科合作，预计将显著提升机器在理解和适应人类环境方面的能力，推动具身人工智能的发展。
🎯 应用场景
该研究的潜在应用领域包括智能机器人、虚拟现实和增强现实等，能够使机器在复杂环境中更好地理解人类的情感和行为。通过引入嗅觉感知，AI系统将能够在更广泛的场景中进行有效的交互和决策，提升用户体验。
📄 摘要（原文）
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.