Active Robot Learning for Temporal Task Models
Loading...
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Authors
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
9
Series
Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, HRI'18, Volume Part F135041, pp. 123-131, ACM/IEEE International Conference on Human-Robot Interaction
Abstract
With the goal of having robots learn new skills after deployment, we propose an active learning framework for modelling user preferences about task execution. The proposed approach interactively gathers information by asking questions expressed in natural language. We study the validity and the learning performance of the proposed approach and two of its variants compared to a passive learning strategy. We further investigate the human-robot-interaction nature of the framework conducting a usability study with 18 subjects. The results show that active strategies are applicable for learning preferences in temporal tasks from non-expert users. Furthermore, the results provide insights in the interaction design of active learning robots.Description
Other note
Citation
Racca, M & Kyrki, V 2018, Active Robot Learning for Temporal Task Models. in Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, HRI'18. vol. Part F135041, ACM/IEEE International Conference on Human-Robot Interaction, ACM, pp. 123-131, ACM/IEEE International Conference on Human-Robot Interaction, Chicago, Illinois, United States, 05/03/2018. https://doi.org/10.1145/3171221.3171241