Abstract
Mobile robots require robust, real-time fault detection capable of continuous adaptation on constrained edge hardware. While deep time-series models excel at unsupervised anomaly detection, their computational cost prohibits high-frequency onboard execution. This paper bridges this gap via a Teacher-Student distillation framework. An offline foundation model (TSPulse) generates pseudo-labels from unlabeled time series augmented with fault injections. A lightweight MiniRocket Student, adapted with a Recursive Least Squares estimator, approximates this complex decision boundary to execute real-time inference onboard. Evaluations on the TSB-AD benchmark and a physical mobile robot demonstrate the Student achieves a 4.30 ms CPU inference latency. During real-world domain shifts, online adaptation enables the Student to recover from unseen mechanical degradation, improving VUS-PR scores from 0.26 to 0.75 without catastrophic forgetting. Crucially, an uncertainty-guided active learning strategy minimizes operator cognitive load, requesting sparse interventions only when encountering novel fault distributions. These results validate the deployment of state-of-the-art anomaly detection on resource-constrained robotics through offline-to-online distillation.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Levy, J., Verstaevel, N., Talon, V., & Gaudou, B. (2026). Continuous Online Fault Detection for Mobile Robots via Adaptive Edge Models. https://omanscience.com/en/articles/continuous-online-fault-detection-for-mobile-robots-via-adaptive-edge-models
MLA 9
Levy, Jordan, et al. "Continuous Online Fault Detection for Mobile Robots via Adaptive Edge Models." https://omanscience.com/en/articles/continuous-online-fault-detection-for-mobile-robots-via-adaptive-edge-models.
Chicago (author–date)
Levy, Jordan, Nicolas Verstaevel, Vincent Talon, and Benoit Gaudou. 2026. "Continuous Online Fault Detection for Mobile Robots via Adaptive Edge Models." https://omanscience.com/en/articles/continuous-online-fault-detection-for-mobile-robots-via-adaptive-edge-models.
Harvard
Levy, J., Verstaevel, N., Talon, V. and Gaudou, B. (2026) 'Continuous Online Fault Detection for Mobile Robots via Adaptive Edge Models', Available at: https://omanscience.com/en/articles/continuous-online-fault-detection-for-mobile-robots-via-adaptive-edge-models.
Vancouver
Levy J, Verstaevel N, Talon V, Gaudou B. Continuous Online Fault Detection for Mobile Robots via Adaptive Edge Models. https://omanscience.com/en/articles/continuous-online-fault-detection-for-mobile-robots-via-adaptive-edge-models
IEEE
J. Levy, N. Verstaevel, V. Talon, and B. Gaudou, "Continuous Online Fault Detection for Mobile Robots via Adaptive Edge Models," https://omanscience.com/en/articles/continuous-online-fault-detection-for-mobile-robots-via-adaptive-edge-models.