Title: Diffusion Graph Neural Networks and Dataset for Robust Olfactory Navigation in Hazard Robotics Show Chinese title

Title: 扩散图神经网络和用于危险机器人中鲁棒嗅觉导航的数据集

Abstract: Navigation by scent is a capability in robotic systems that is rising in demand. However, current methods often suffer from ambiguities, particularly when robots misattribute odours to incorrect objects due to limitations in olfactory datasets and sensor resolutions. To address challenges in olfactory navigation, we introduce a multimodal olfaction dataset along with a novel machine learning method using diffusion-based molecular generation that can be used by itself or with automated olfactory dataset construction pipelines. This generative process of our diffusion model expands the chemical space beyond the limitations of both current olfactory datasets and training methods, enabling the identification of potential odourant molecules not previously documented. The generated molecules can then be more accurately validated using advanced olfactory sensors, enabling them to detect more compounds and inform better hardware design. By integrating visual analysis, language processing, and molecular generation, our framework enhances the ability of olfaction-vision models on robots to accurately associate odours with their correct sources, thereby improving navigation and decision-making through better sensor selection for a target compound in critical applications such as explosives detection, narcotics screening, and search and rescue. Our methodology represents a foundational advancement in the field of artificial olfaction, offering a scalable solution to challenges posed by limited olfactory data and sensor ambiguities. Code, models, and data are made available to the community at: https://huggingface.co/datasets/kordelfrance/olfaction-vision-language-dataset. Show Chinese abstract

Abstract: 通过气味进行导航是机器人系统中一种日益受到关注的能力。 然而，当前的方法经常面临歧义问题，特别是在机器人由于嗅觉数据集和传感器分辨率的限制而将气味错误地归因于不正确的物体时。 为了解决嗅觉导航中的挑战，我们引入了一个多模态嗅觉数据集，以及一种基于扩散的分子生成的新型机器学习方法，该方法可以单独使用或与自动嗅觉数据集构建管道结合使用。 我们的扩散模型的生成过程扩展了化学空间，超越了当前嗅觉数据集和训练方法的限制，使能够识别之前未记录的潜在气味分子。 然后可以使用先进的嗅觉传感器更准确地验证生成的分子，使其能够检测更多化合物，并指导更好的硬件设计。 通过整合视觉分析、语言处理和分子生成，我们的框架增强了机器人嗅觉-视觉模型准确将气味与其正确来源相关联的能力，从而在诸如爆炸物检测、毒品筛查和搜救等关键应用中通过为特定化合物选择更好的传感器来提高导航和决策能力。 我们的方法代表了人工嗅觉领域的一项基础性进展，提供了一种可扩展的解决方案，以应对有限的嗅觉数据和传感器歧义所带来的挑战。 代码、模型和数据可在以下网址提供给社区：https://huggingface.co/datasets/kordelfrance/olfaction-vision-language-dataset.