Anomaly detection in industrial robotics: A novel dataset and benchmark method

Industrial robotic manipulators enhance manufacturing productivity and safety, but can experience anomalies that disrupt production and compromise safety. The complexity and high variability of modern robotics applications make anomaly detection (AD) particularly challenging. Data-driven semi-supervised approaches are emerging as a promising solution for this task. To overcome the lack of sufficiently rich labeled datasets, models are trained using only normal data, and anomalies are identified by detecting deviations from the normal distribution. To ensure reliable deployment of this technology, it is crucial to design models that are robust against variations in working conditions. At the same time, the availability of public datasets with data from actual systems is fundamental to compare different strategies. In this paper, we introduce Mitsubishi Electric Anomaly Detection (ME-AD), a novel dataset with data capturing real-world, progressively evolving mechanical faults in robotic manipulators during pick-and-place operations. Unlike existing datasets, ME-AD provides authentic sensor data documenting gradual joint degradation up to a mechanical fault during a complete run-to-failure. Furthermore, we propose a Gaussian Process Regression (GPR)-based anomaly detection method that models robot dynamics and employs a novel uncertainty-aware anomaly score to detect anomalies, offering generalization across unseen motion conditions. Our extensive evaluations demonstrate strong anomaly-detection performance and cross-trajectory transfer to out-of-distribution motion profiles and execution speeds.