Accurate material identification plays a crucial role in embodied AI systems, enabling a wide range of applications. However, current vision-based solutions are limited by the inherent constraints of optical sensors, while radio-frequency (RF) approaches, which can reveal intrinsic material properties, have received growing attention. Despite this progress, RF-based material identification remains hindered by the lack of large-scale public datasets and the limited benchmarking of learning-based approaches. In this work, we present RF-MatID, the first open-source, large-scale, wide-band, and geometry-diverse RF dataset for fine-grained material identification. RF-MatID includes 16 fine-grained categories grouped into 5 superclasses, spanning a broad frequency range from 4 to 43.5 GHz, and comprises 142k samples in both frequency- and time-domain representations. The dataset systematically incorporates controlled geometry perturbations, including variations in incidence angle and stand-off distance. We further establish a multi-setting, multi-protocol benchmark by evaluating state-of-the-art deep learning models, assessing both in-distribution performance and out-of-distribution robustness under cross-angle and cross-distance shifts. The 5 frequency-allocation protocols enable systematic frequency- and region-level analysis, thereby facilitating real-world deployment. RF-MatID aims to enable reproducible research, accelerate algorithmic advancement, foster cross-domain robustness, and support the development of real-world application in RF-based material identification.

翻译：精确的材料识别在具身人工智能系统中扮演着关键角色，并支撑着广泛的应用。然而，当前基于视觉的解决方案受限于光学传感器的固有约束，而能够揭示材料内在特性的射频方法正受到越来越多的关注。尽管取得了这些进展，射频材料识别仍因缺乏大规模公开数据集以及基于学习的方法的基准测试有限而受到阻碍。在本工作中，我们提出了RF-MatID，这是首个用于细粒度材料识别的开源、大规模、宽频带且几何多样化的射频数据集。RF-MatID包含16个细粒度类别，这些类别被归入5个超类，覆盖了从4 GHz到43.5 GHz的宽频率范围，并包含14.2万个样本，同时提供频域和时域表示。该数据集系统地纳入了受控的几何扰动，包括入射角和探测距离的变化。我们进一步通过评估最先进的深度学习模型，建立了多设置、多协议的基准，评估了其在分布内性能以及在跨角度和跨距离偏移下的分布外鲁棒性。5种频率分配协议支持系统的频率级和区域级分析，从而促进实际部署。RF-MatID旨在推动可复现的研究，加速算法进步，增强跨领域鲁棒性，并支持射频材料识别在实际应用中的发展。