Online Continual Learning for Automatic Error Prevention in Data-Driven HRI

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 paper, 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.