Zero-Shot Learning to Enable Error Awareness in Data-Driven HRI

Data-driven social imitation learning is a minimally-supervised approach to generate robot behaviors for human-robot interaction (HRI). However, this type of learning-based approach is error-prone. Existing error detection methods for HRI rely on data labeling, rendering them inappropriate for the data-driven paradigm. We present a zero-shot error detection strategy that requires no labeled data. We use human interaction data to learn models of normal human behavior, then use these models to extract features that help discriminate abnormal human reactions to robot errors. In this feature space, we frame error detection as a novelty detection task, utilizing human interaction data to learn a model of non-erroneous interactions in an unsupervised fashion. Then, we apply the fitted novelty detector to HRI data to identify erroneous robot behavior. We show that our method obtains an average precision of 0.497 on errors, outperforming unsupervised baselines and supervised approaches with limited training data.