Active Incremental Learning of a Contextual Skill Model

Murtaza Hazara, Xiaopu Li, Ville Kyrki

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

2 Sitaatiot (Scopus)
198 Lataukset (Pure)

Abstrakti

Contextual skill models are learned to provide skills over a range of task parameters, often using regression across optimal task-specific policies. However, the sequential nature of the learning process is usually neglected. In this paper, we propose to use active incremental learning by selecting a task which maximizes performance improvement over entire task set. The proposed framework exploits knowledge of individual tasks accumulated in a database and shares it among the tasks using a contextual skill model. The framework is agnostic to the type of policy representation, skill model, and policy search. We evaluated the skill improvement rate in two tasks, ball-in-a-cup and basketball. In both, active selection of tasks lead to a consistent improvement in skill performance over a baseline.
AlkuperäiskieliEnglanti
OtsikkoProceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
KustantajaIEEE
Sivut1834-1839
Sivumäärä6
ISBN (elektroninen)978-1-7281-4004-9
DOI - pysyväislinkit
TilaJulkaistu - 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE/RSJ International Conference on Intelligent Robots and Systems - The Venetian Macao, Macau, Kiina
Kesto: 4 marrask. 20198 marrask. 2019
https://www.iros2019.org/

Julkaisusarja

NimiProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
KustantajaIEEE
ISSN (painettu)2153-0858
ISSN (elektroninen)2153-0866

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
LyhennettäIROS
Maa/AlueKiina
KaupunkiMacau
Ajanjakso04/11/201908/11/2019
www-osoite

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