Active Incremental Learning of a Contextual Skill Model

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A4 Artikkeli konferenssijulkaisussa
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Date

2019

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Department of Electrical Engineering and Automation

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Language

en

Pages

6

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Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019, pp. 1834-1839, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems

Abstract

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.

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Keywords

Active incrementl learning, Contextual skill model, Task parameters, Optima task-specific policies, Learning process, Skill improvement rate, Skill performance, Policy representation, Policy search, Ball-in-a-cup, Basketball, Learning (artificial intelligence)

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DOI

10.1109/IROS40897.2019.8967837

Citation

Hazara, M, Li, X & Kyrki, V 2019, Active Incremental Learning of a Contextual Skill Model . in Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 ., 8967837, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, pp. 1834-1839, IEEE/RSJ International Conference on Intelligent Robots and Systems, Macau, China, 04/11/2019 . https://doi.org/10.1109/IROS40897.2019.8967837

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https://urn.fi/URN:NBN:fi:aalto-202002122149

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[article-cris] Sähkötekniikan korkeakoulu / ELEC