Abstract
Human-Robot Collaboration (HRC) has been widely used in daily life and industry for maximizing the advantages of humans and robots, respectively. However, the internal modeling errors or external perturbations still affect robotic systems such as human collisions and environmental changes. Multimodal anomaly detection plays an increasingly important role in HRC applications, which detects unexpected anomalies from multimodal signals. Due to the complex temporal dependence and stochasticity, it is still difficult to choose a common model applicable to all collaborative tasks, and lack of comparative analysis of existing methods and verification of specific application cases. In this paper, six representative deep learning-based methods are evaluated and the comparing metrics including detection accuracy, multi-modality combinations, and anomaly time bias. For a fair comparison, each detector models multimodal signals from non-anomalous samples and then determines an anomaly using a predefined threshold. We evaluate the detectors with force, torque, velocity, tactile, and kinematic sensing during a human-robot kitting experiment that consists of six individual skills, results indicate that the LSTM-DAGMM based detector outperformed the others, which yielding higher accuracy and efficiency. The metrics are measured with the RUC and ROC by changing the settings of multi-modality combinations and various anomaly biases, which aim to obtain the best performance of multimodal anomaly detection.