  During the operation of industrial robots, unusual events may endanger the
safety of humans and the quality of production. When collecting data to detect
such cases, it is not ensured that data from all potentially occurring errors
is included as unforeseeable events may happen over time. Therefore, anomaly
detection (AD) delivers a practical solution, using only normal data to learn
to detect unusual events. We introduce a dataset that allows training and
benchmarking of anomaly detection methods for robotic applications based on
machine data which will be made publicly available to the research community.
As a typical robot task the dataset includes a pick-and-place application which
involves movement, actions of the end effector and interactions with the
objects of the environment. Since several of the contained anomalies are not
task-specific but general, evaluations on our dataset are transferable to other
robotics applications as well. Additionally, we present MVT-Flow (multivariate
time-series flow) as a new baseline method for anomaly detection: It relies on
deep-learning-based density estimation with normalizing flows, tailored to the
data domain by taking its structure into account for the architecture. Our
evaluation shows that MVT-Flow outperforms baselines from previous work by a
large margin of 6.2% in area under ROC.
