In robotic systems, both software and hardware components are equally important. However, scant attention has been devoted until now in order to detect anomalies/failures affecting the software component of robots while many proposals exist aimed at detecting physical anomalies. To bridge this gap, the present paper focuses on the study of anomalies affecting the software performance of a robot by using a novel visualization tool. Unsupervised visualization methods from the machine learning field are applied in order to upgrade the recently proposed Hybrid Unsupervised Exploratory Plots (HUEPs). Furthermore, Curvilinear Component Analysis and t-distributed stochastic neighbor embedding are added to the original HUEPs formulation and comprehensively compared. Furthermore, all the different combinations of HUEPs are validated in a real-life scenario. Thanks to this intelligent visualization of robot status, interesting conclusions can be obtained to improve anomaly detection in robot performance.
