Affect-driven Robot Behavior Learning System using EEG Signals for Less Negative Feelings and More Positive Outcomes

Abstract

Learning from human feedback using event-related electroencephalography (EEG) signals has attracted extensive attention recently owing to their intuitive communication ability by decoding user intentions. However, this approach requires users to perform specified tasks and their success or failure. In addition, the amount of attention needed for decision-making increases with the task difficulty, decreasing human feedback quality over time because of fatigue. Consequently, this can reduce the interaction quality and can even cause interaction breakdowns. To overcome these limitations and enable the interaction of robots with higher complexity tasks, we propose a closed-loop control system that learns affective responses to robot behaviors and provides natural feedback to optimize robot parameters for smoothing the next action. Experimental results demonstrate our affect-driven closed-loop control system yielded better affective outcomes and task performance than an open-loop system with correlated neuroscientific characteristics of EEG signals, thus enhancing the quality of human-robot interaction.