[2603.23342] Edge Radar Material Classification Under Geometry Shifts

Computer Science > Robotics arXiv:2603.23342 (cs) [Submitted on 24 Mar 2026] Title:Edge Radar Material Classification Under Geometry Shifts Authors:Jannik Hohmann, Dong Wang, Andreas Nüchter View a PDF of the paper titled Edge Radar Material Classification Under Geometry Shifts, by Jannik Hohmann and 2 other authors View PDF HTML (experimental) Abstract: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. Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.23342 [cs.RO]   (or arXiv:2603.23342v1 [cs.RO] for this version)   https://doi.org/10.48550/arXiv.2603.23342 Focus to learn more arXiv-iss...