TY - GEN
T1 - Not all users are the same
T2 - 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
AU - Soni, Utkarsh
AU - Sreedharan, Sarath
AU - Kambhampati, Subbarao
N1 - Funding Information:
This research is supported in part by ONR grants N00014-16-1-2892, N00014-18-1-2442, N00014-18-1-2840, N00014-9-1-2119, AFOSR grant FA9550-18-1-0067, DARPA SAIL-ON grant W911NF-19-2-0006, and a JP Morgan AI Faculty Research grant.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - There is a growing interest in designing robots that can work alongside humans. Such robots will undoubtedly be expected to explain their behavior and decisions. While generating explanations is an actively researched topic, most works tend to focus on methods that generate explanations that are one size fits all. As in the specifics of the user-model are completely ignored. The handful of works that look at tailoring their explanation to the user's background rely on having specific models of the users (either analytic models or learned labeling models). The goal of this work is thus to propose an end-to-end adaptive explanation generation system that begins by learning the different types of users that the robot could interact with. Then during the interaction with the target user, it is tasked with identifying the type on the fly and adjust its explanations accordingly. The former is achieved by a data-driven clustering approach while for the latter, we compile our explanation generation problem into a POMDP. We demonstrate the usefulness of our system on two domains using state-of-the-art POMDP solvers. We also report the results of a user study that investigates the benefits of providing personalized explanations in a human-robot interaction setting.
AB - There is a growing interest in designing robots that can work alongside humans. Such robots will undoubtedly be expected to explain their behavior and decisions. While generating explanations is an actively researched topic, most works tend to focus on methods that generate explanations that are one size fits all. As in the specifics of the user-model are completely ignored. The handful of works that look at tailoring their explanation to the user's background rely on having specific models of the users (either analytic models or learned labeling models). The goal of this work is thus to propose an end-to-end adaptive explanation generation system that begins by learning the different types of users that the robot could interact with. Then during the interaction with the target user, it is tasked with identifying the type on the fly and adjust its explanations accordingly. The former is achieved by a data-driven clustering approach while for the latter, we compile our explanation generation problem into a POMDP. We demonstrate the usefulness of our system on two domains using state-of-the-art POMDP solvers. We also report the results of a user study that investigates the benefits of providing personalized explanations in a human-robot interaction setting.
UR - http://www.scopus.com/inward/record.url?scp=85124358438&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85124358438&partnerID=8YFLogxK
U2 - 10.1109/IROS51168.2021.9636331
DO - 10.1109/IROS51168.2021.9636331
M3 - Conference contribution
AN - SCOPUS:85124358438
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 6240
EP - 6247
BT - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 September 2021 through 1 October 2021
ER -