Active Incremental Learning of a Contextual Skill Model

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Conference article in proceedings
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Date
2019
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Language
en
Pages
6
Series
Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019, 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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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