Generating Natural Language Responses in Robot-mediated Referential Communication Tasks to Simulate Theory of Mind
Z. Liu*, Y. Qin*, H. Zou*, E. Paek, D. Casenhiser, W. Zhou and X. Zhao., in Social Robotics, 2022. (* denotes equal contribution).
Keywords: Human-Robot Interaction, Theory of Mind, Social Assistive Robots, Conversational Agent
With advances in neural network-based computation, socially assistive robots have been endowed with the ability to provide natural conversation to users. However, the lack of transparency in the computation models results in unexpected robot behaviors and feedback, which may cause users to lose their trust in the robot. Theory of mind (ToM) in cooperative tasks has been considered as a key factor in understanding the relationship between user acceptance and the explainability of robot behaviors. Therefore, we develop a dialog system using previously collected data from a robot-mediated cooperative communication task data to simulate natural language smart feedback. The system is designed based on the mechanism of ToM and validated with a simu- lation test. Based on the result, we believe the designed dialog system bears the feasibility of simulating ToM and can be used as a research tool for further studying the importance of simulating ToM in human-robot communication.
This paper is accepted by the 14th International Conference on Social Robotics. The first three authors contributed equally to this work.