An Experimental Comparison of Anomaly Detection Methods for Collaborative Robot Manipulators

A large number of methods for anomaly detection in robotic manipulation have been proposed, but their applicability and performance in real-world scenarios are often not established. In this paper, we therefore perform an experimental comparison of a broad range of practically applicable methods to detect exogenous anomalies in pick-and-place tasks. We first collect a dataset, which has been made freely available, on a state-of-the-art collaborative robot. The extensive experimental campaign comprises 600 runs under normal operation, and 80 runs where exogenous perturbations are present, and data is collected on the robot's joints, force-torque readings, and the time spent in each program node. Using the data collected during normal operation, we train a set of anomaly detection methods whose computational complexity is low enough to run on resource-constrained robot hardware. We then evaluate the trained methods on the data collected in the perturbed runs and find that several methods can achieve a high anomaly detection performance. We show that exploiting knowledge of the robot's program tree can increase the performance for some types of anomalies. We also observe that performance, in general, deteriorates when the application includes: (i) rarely-visited program branches, (ii) physical contact with the environment, and (iii) stochastic trajectories.