Adapting Robot Behavior using Regulatory Focus Theory, User Physiological State and Task-Performance Information

Social robots are expected to be part of everyday life of people. This will generate interactions between humans and robots that may have positive or negative effects on the users. In order to minimize the negative effects and increase robot persuasiveness, robots should behave in an appropriate manner by adapting to their users. How to achieve this adaptation remains a challenge. We propose the usage of the Regulatory Focus Theory, user physiological state, and game-performance information in order to detect user stress and adapt the behavior of the robot. We present a longitudinal experiment conducted with 35 participants in a game-like scenario. The robot was trained for adapting to the regulatory focus of the users and decreasing their stress while they were playing the game. For this reason, we trained the robot with 12 participants with Chronic Promotion State and with 12 participants with Chronic Prevention State. We used a Q-Learning algorithm based on the Regulatory Focus of the participants, user stress, and task performance. The model obtained was tested with 2 groups (6 and 5 participants, respectively) according to their Chronic Regulatory Focus. Results show that our system was able to generate a robot behavior capable of increasing robot persuasiveness and reducing user stress, which is of great importance for social robots.