A Minimally Supervised Approach Based on Variational Autoencoders for Anomaly Detection in Autonomous Robots

Abstract

Detection of anomalies and faults is a crucial ability for fully autonomous robots. This letter proposes a new deep learning-based minimally supervised method for detecting anomalies in autonomous robots. We contribute a new Variational Auto-Encoder architecture able to model very long multivariate sensor logs exploiting a new incremental training method, which induces a progress-based latent space that can be used to detect anomalies both at runtime and offline. While most existing approaches are trained in a semi-supervised fashion and require big batches of nominal observations, our method is trained using unlabeled observations of a robot performing a task, containing both nominal and anomalous executions. Only a very little amount (even just one) of labeled nominal executions is then required to partition the learned latent space into nominal and anomalous regions. Experimental results show that our method outperforms state-of-the-art anomaly detectors commonly used in robotics both in terms of false positive rate and alert delay.