Episodic memory formulation and its application in long-term HRI

摘要

Efficient storing and managing of robot's experiences is of utmost importance in long-term recurring HumanRobot Interaction scenarios, where the volume of information increases constantly. To address these issues, a novel entity-based episodic memory is introduced in this work. Knowledge is represented by hierarchical multigraphs enabling for fast information retrieval. Consisting entities are asynchronously updated, while a time-correlated importance factor modulates the merging, forgetting or refreshing of memories, in order to facilitate search and management of the stored information. An HMM-based probabilistic inference is employed to infer or predict the HRI state or to identify abnormal scenario unfolding and, thus, guide future robot activities. The performance of the employed memory schema is assessed on both simulated and real “breakfast preparation” scenarios. The results indicate that the proposed memory model is able to efficiently store and manage the acquired data without any loss of critical information. Moreover, our approach was also shown capable of successfully inferring user's hidden preferences and thus guiding robot behavior accordingly in order to improve user's HRI experience.