Detecting Execution Anomalies As an Oracle for Autonomy Software Robustness

We propose a method for detecting execution anomalies in robotics and autonomy software. The algorithm uses system monitoring techniques to obtain profiles of executions. It uses a clustering algorithm to create clusters of those executions, representing nominal execution. A distance metric determines whether additional execution profiles belong to the existing clusters or should be considered anomalies. The method is suitable for identifying faults in robotics and autonomy systems. We evaluate the technique in simulation on two robotics systems, one of which is a real-world industrial system. We find that our technique works well to detect possibly unsafe behavior in autonomous systems.