mlsanityData to 14 Oct 2026

Continuous Online Fault Detection for Mobile Robots via Adaptive Edge Models

arXiv 2609.29194 ↗First posted 24 Sep 2026cs.RO cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 25 references that are in the map. First posted on arXiv 24 Sep 2026.

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References · 11

  1. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,330
  2. 2020Learning under Concept Drift: A Reviewcited by 477
  3. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  4. 2020MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classificationcited by 190
  5. 2023Bake off redux: a review and experimental evaluation of recent time series classification algorithmscited by 101
  6. 2023Deep Learning for Time Series Classification and Extrinsic Regression: A Current Surveycited by 68
  7. 2024aeon: a Python toolkit for learning from time seriescited by 47
  8. 2024Dive into Time-Series Anomaly Detection: A Decade Reviewcited by 24
  9. 2025TSPulse: Tiny Pre-Trained Models with Disentangled Representations for Rapid Time-Series Analysiscited by 12
  10. 2025World Models for Anomaly Detection during Model-Based Reinforcement Learning Inferencecited by 3
  11. 2025Multimodal Anomaly Detection with a Mixture-of-Expertscited by 3

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