A computational model of human decision making and learning for assessment of co-adaptation in neuro-adaptive human-robot interaction

Studies have demonstrated the potential of using error-related potentials (ErrPs), online decoded from the electroencephalogram (EEG) of a human observer, for robot skill learning and mediation of co-adaptation in collaborative human-robot interaction (HRI). While these studies provided proof-of-concept of this approach as a highly promising avenue in the field of HRI, a systematic understanding of the dyadic interacting system (human and machine) remained unexplored. This research aims to address this gap by proposing a computational model of the human counterpart and simulating the integrated dyadic system. The model can be employed for the systematic study of both human behavioral and technical factors influencing co-adaptation as exemplarily demonstrated in this paper for hypothetical variations of ErrP-decoder performance. The obtained findings have practical implications for future steps along this line of research, for instance to what extent and how improvements of ErrP-decoder performance can benefit co-adaptation in ErrP-based HRI. The proposed computational model enables the prediction of human behavior in the context of ErrP-based HRI. As such it allows the simulation of future empirical studies prior to their conductance and thereby providing a means for accelerating progress along this line of research in a resource-saving manner.