Data-driven social imitation learning is a minimally supervised approach to generating robot behaviors for human–robot interaction (HRI). However, like any learning-based approach, there are inherent challenges and potential errors. Existing strategies for error prevention in HRI predominantly rely on handcrafted methods and predefined design guidelines, with limited focus on learning-based approaches. In this article, we present an autonomous, online continual learning strategy for data-driven HRI that uses error detections to facilitate error prevention. We leverage error detections as negative training signals, which dynamically update the data-driven robot action model parameters, guiding it away from repeating errors in future interactions. This approach modifies the standard (offline) data-driven HRI training objective to continually refine the robot’s performance in an online manner. We use simulated HRI data to show that our error prevention method can effectively reduce the average rate of robot errors in HRI by up to 6.68%. Furthermore, even with a limited-precision error detector, our method can achieve a 4.68% error rate reduction, paving the way towards a completely autonomous error management framework for data-driven HRI.

Data-driven social imitation learning is a minimally-supervised approach to generating 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.