Name
Title | ||
|---|---|---|
A computational model of human decision making and learning for assessment of co-adaptation in neuro-adaptive human-robot interaction |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SMC.2019.8913872 | 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) |
Keywords | Field | DocType |
human machine,computational model,human counterpart,integrated dyadic system,human behavioral,ErrP -decoder performance,HRI,human behavior,future empirical studies,human decision making,neuro-adaptive human-robot interaction,error-related potentials,human observer,robot skill learning,mediation,highly promising avenue,systematic understanding,dyadic interacting system | Co-adaptation,Computer science,Artificial intelligence,Machine learning,Human–robot interaction | Conference |
ISSN | ISBN | Citations |
1062-922X | 978-1-7281-4570-9 | 0 |
PageRank | References | Authors |
0.34 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan K. Ehrlich | 1 | 0 | 1.01 |
| Gordon Cheng | 2 | 1250 | 115.33 |