RoBiGAN: A bidirectional Wasserstein GAN approach for online robot fault diagnosis via internal anomaly detection

Complex robots in challenging scenarios require constant monitoring of their state and adaptation of their behavior to ensure robustness, reliability and longevity. While known possible errors can be specifically surveilled, other prob-lems can be fully unforeseen, requiring detection systems able to identify novel faults. We detect possible faults as anomalies on various internal sensor data, utilizing unsupervised learning techniques. A bidirectional Wasserstein GAN approach for anomaly detection on multivariate, highly dependent time-series data is implemented and trained on a small amount of non-anomalous robot sensor data. This model is then used for inference on the on-board hardware of a robot without parallel processing units. We evaluate multiple variants of the architecture using manually introduced anomalies in the form of different weights attached to the robot's legs. Overall we are able to show that RoBiGAN is able to consistently detect and localize small anomalies in an online scenario, with little to no robot specific modeling needed.