TY - GEN
T1 - Exploring embodiment and dueling bandit learning for preference adaptation in human-robot interaction
AU - Schneider, Sebastian
AU - Kummert, Franz
N1 - Funding Information:
ACKNOWLEDGMENT This research was funded by grants from the Cluster of Excellence Cognitive Interaction Technology ’CITEC’ (EXC 277), Bielefeld University.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/8
Y1 - 2017/12/8
N2 - Adaptation for social companions is a crucial requirement for future applications. Personalized interaction seems to be an important factor for long-Term commitment to interact with a social robot. We present a study evaluating the feasibility of a dueling bandit learning approach for preference learning (PL) in Human-Robot Interaction (HRI). Furthermore, we explore whether the embodiment of the PL agent has an influence on the user's evaluation of the learner. We conducted a study (n=53) comparing a graphical user interface (GUI), a virtual robot and a real robot. We found no difference regarding the preference for the virtual or real robot. We used the obtained study data to compare the PL approach against a strategy that randomly selects preference rankings. The results show that that the dueling bandit PL approach can be used to learn a user's preference in HRI.
AB - Adaptation for social companions is a crucial requirement for future applications. Personalized interaction seems to be an important factor for long-Term commitment to interact with a social robot. We present a study evaluating the feasibility of a dueling bandit learning approach for preference learning (PL) in Human-Robot Interaction (HRI). Furthermore, we explore whether the embodiment of the PL agent has an influence on the user's evaluation of the learner. We conducted a study (n=53) comparing a graphical user interface (GUI), a virtual robot and a real robot. We found no difference regarding the preference for the virtual or real robot. We used the obtained study data to compare the PL approach against a strategy that randomly selects preference rankings. The results show that that the dueling bandit PL approach can be used to learn a user's preference in HRI.
UR - http://www.scopus.com/inward/record.url?scp=85045769667&partnerID=8YFLogxK
U2 - 10.1109/ROMAN.2017.8172476
DO - 10.1109/ROMAN.2017.8172476
M3 - Conference contribution
AN - SCOPUS:85045769667
T3 - RO-MAN 2017 - 26th IEEE International Symposium on Robot and Human Interactive Communication
SP - 1325
EP - 1331
BT - RO-MAN 2017 - 26th IEEE International Symposium on Robot and Human Interactive Communication
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 August 2017 through 1 September 2017
ER -