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.

翻译：在工业机器人运行过程中，异常事件可能危及人员安全与生产质量。在采集用于检测此类异常事件的数据时，由于随时间推移可能发生不可预见的情况，无法确保包含所有潜在故障类型的数据。因此，异常检测（AD）提供了一种实用解决方案——仅通过正常数据学习便可检测异常事件。我们引入一个基于机器数据的机器人应用异常检测方法训练与基准测试数据集，该数据集将向学术界公开。作为典型机器人任务，该数据集包含拾取与放置应用，涉及运动、末端执行器动作及与环境中物体的交互。由于其中多个异常并非特定于该任务，具有通用性，因此基于我们数据集的评估结果可迁移至其他机器人应用场景。此外，我们提出MVT-Flow（多元时间序列流）作为异常检测的新基线方法：该方法基于归一化流进行深度学习密度估计，通过考虑数据域结构进行架构设计。评估表明，MVT-Flow在ROC曲线下面积指标上以6.2%的显著优势超越先前工作的基线方法。