A Systems-Theoretic Approach to Mental State Estimation for Theory-of-Mind-Aware Social Robots

Abstract

Social robots are increasingly deployed in fields such as health care and education to support users through social interactions. Nonetheless, these robots mostly rely on black-box machine learning methods that lack awareness of the mental states of their users, which often leads to unnatural behavior. To address this, we propose three model-based techniques for real-time estimation of invisible mental states of humans. Each method adapts the extended Kalman filter and incorporates a validated dynamic model of human mental states. These mental state estimators are designed for human-robot social interactions and personalize their parameters using initial user data. When tested with 10 human participants interacting with a NAO robot, the mental state estimators reduced the average error in estimation and prediction of mental states across all participants by 3% (i.e., from 12% to 9%), with improvements of up to 13% for individual participants. These results demonstrate the potential of integrating such state estimators into the behavioral control systems of social robots to enhance their awareness of the mental states of users.