Continuous Online Fault Detection for Mobile Robots via Adaptive Edge Models
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 demonstrat...
| Authors | Jordan Levy, Nicolas Verstaevel, Vincent Talon et al. |
|---|---|
| Primary category | cs.RO |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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