Material awareness can improve robotic navigation and interaction, particularly in conditions where cameras and LiDAR degrade. We present a lightweight mmWave radar material classification pipeline designed for ultra-low-power edge devices (TI IWRL6432), using compact range-bin intensity descriptors and a Multilayer Perceptron (MLP) for real-time inference. While the classifier reaches a macro-F1 of 94.2\% under the nominal training geometry, we observe a pronounced performance drop under realistic geometry shifts, including sensor height changes and small tilt angles. These perturbations induce systematic intensity scaling and angle-dependent radar cross section (RCS) effects, pushing features out of distribution and reducing macro-F1 to around 68.5\%. We analyze these failure modes and outline practical directions for improving robustness with normalization, geometry augmentation, and motion-aware features.

翻译：材料感知能力可提升机器人导航与交互性能，尤其在相机与激光雷达性能退化条件下。我们提出一种面向超低功耗边缘设备（TI IWRL6432）的轻量级毫米波雷达材料分类流水线，采用紧凑型距离单元强度描述子与多层感知机（MLP）实现实时推理。尽管该分类器在名义训练几何条件下宏F1分数达到94.2%，但在实际几何偏移（包括传感器高度变化与微小倾斜角度）下观察到显著性能下降。此类扰动引发系统性强度缩放及角度依赖的雷达散射截面（RCS）效应，导致特征偏离数据分布，使宏F1分数降至约68.5%。我们分析了这些失效模式，并提出了通过归一化、几何增广及运动感知特征提升鲁棒性的实用方向。