OBJECTIVE: Error-related potentials (ErrP) have been proposed as an intuitive feedback signal decoded from the ongoing electroencephalogram (EEG) of a human observer for improving human-robot interaction (HRI). While recent demonstrations of this approach have successfully studied the use of ErrPs as a teaching signal for robot skill learning, so far, no efforts have been made towards HRI scenarios where mutual adaptations between human and robot are expected or required. These are collaborative or social interactive scenarios without predefined dominancy of the human partner and robots being perceived as intentional agents. Here we explore the usability of ErrPs as a feedback signal from the human for mediating co-adaptation in human-robot interaction. APPROACH: We experimentally demonstrate ErrPs-based mediation of co-adaptation in a human-robot interaction study where successful interaction depended on co-adaptive convergence to a consensus between them. While subjects adapted to the robot by reflecting upon its behavior, the robot adapted its behavior based on ErrPs decoded online from the human partner's ongoing EEG. MAIN RESULTS: ErrPs were decoded online in single trial with an avg. accuracy of 81.8% ± 8.0% across 13 subjects, which was sufficient for effective adaptation of robot behavior. Successful co-adaptation was demonstrated by significant improvements in human-robot interaction efficacy and efficiency, and by the robot behavior that emerged during co-adaptation. These results indicate the potential of ErrPs as a useful feedback signal for mediating co-adaptation in human-robot interaction as demonstrated in a practical example. SIGNIFICANCE: As robots become more widely embedded in society, methods for aligning them to human expectations and conventions will become increasingly important in the future. In this quest, ErrPs may constitute a promising complementary feedback signal for guiding adaptations towards human preferences. In this paper we extended previous research to less constrained HRI scenarios where mutual adaptations between human and robot are expected or required.
OBJECTIVE: Error-related potentials (ErrP) have been proposed as an intuitive feedback signal decoded from the ongoing electroencephalogram (EEG) of a human observer for improving human-robot interaction (HRI). While recent demonstrations of this approach have successfully studied the use of ErrPs as a teaching signal for robot skill learning, so far, no efforts have been made towards HRI scenarios where mutual adaptations between human and robot are expected or required. These are collaborative or social interactive scenarios without predefined dominancy of the human partner and robots being perceived as intentional agents. Here we explore the usability of ErrPs as a feedback signal from the human for mediating co-adaptation in human-robot interaction. APPROACH: We experimentally demonstrate ErrPs-based mediation of co-adaptation in a human-robot interaction study where successful interaction depended on co-adaptive convergence to a consensus between them. While subjects adapted to the robot by reflecting upon its behavior, the robot adapted its behavior based on ErrPs decoded online from the human partner's ongoing EEG. MAIN RESULTS: ErrPs were decoded online in single trial with an avg. accuracy of 81.8% ± 8.0% across 13 subjects, which was sufficient for effective adaptation of robot behavior. Successful co-adaptation was demonstrated by significant improvements in human-robot interaction efficacy and efficiency, and by the robot behavior that emerged during co-adaptation. These results indicate the potential of ErrPs as a useful feedback signal for mediating co-adaptation in human-robot interaction as demonstrated in a practical example. SIGNIFICANCE: As robots become more widely embedded in society, methods for aligning them to human expectations and conventions will become increasingly important in the future. In this quest, ErrPs may constitute a promising complementary feedback signal for guiding adaptations towards human preferences. In this paper we extended previous research to less constrained HRI scenarios where mutual adaptations between human and robot are expected or required.