TY - GEN
T1 - To ask or not to ask
T2 - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
AU - Gucsi, Balint
AU - Tarapore, Danesh S.
AU - Yeoh, William
AU - Amato, Christopher
AU - Tran-Thanh, Long
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/24
Y1 - 2020/10/24
N2 - In this paper we investigate how social robots can efficiently gather user preferences without exceeding the allowed user annoyance threshold. To do so, we use a Gazebo based simulated office environment with a TIAGo Steel robot. We then formulate the user annoyance aware preference elicitation problem as a combination of tensor completion and knapsack problems. We then test our approach on the aforementioned simulated environment and demonstrate that it can accurately estimate user preferences.
AB - In this paper we investigate how social robots can efficiently gather user preferences without exceeding the allowed user annoyance threshold. To do so, we use a Gazebo based simulated office environment with a TIAGo Steel robot. We then formulate the user annoyance aware preference elicitation problem as a combination of tensor completion and knapsack problems. We then test our approach on the aforementioned simulated environment and demonstrate that it can accurately estimate user preferences.
UR - https://www.scopus.com/pages/publications/85102396505
U2 - 10.1109/IROS45743.2020.9341607
DO - 10.1109/IROS45743.2020.9341607
M3 - Conference contribution
AN - SCOPUS:85102396505
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 7935
EP - 7940
BT - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 October 2020 through 24 January 2021
ER -