Davide Azzalini

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

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