arXiv Machine Learning September 25, 2026 By Jordan Levy, Nicolas Verstaevel, Vincent Talon, Benoit Gaudou

Continuous Online Fault Detection for Mobile Robots via Adaptive Edge Models

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The paper presents a Teacher-Student distillation framework for continuous online fault detection in mobile robots. An offline foundation model (TSPulse) generates pseudo‑labels from augmented time‑series data, while a lightweight MiniRocket Student, enhanced with a Recursive Least Squares estimator, performs real‑time inference with a 4.30 ms CPU latency. The Student adapts online to domain shifts, improving VUS‑PR scores from 0.26 to 0.75 and uses an uncertainty‑guided active learning strategy to request minimal operator interventions.

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