2019年1月28日
An incremental episodic memory framework for topological map building
International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2018 - Proceedings
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- 開始ページ
- 322
- 終了ページ
- 327
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/KCIC.2018.8628468
- 出版者・発行元
- IEEE
In this paper, an episodic memory learning framework is proposed for categorizing and encoding sensory information that acquired from a robot for environment adaptation and sensorimotor map building. The proposed learning model termed as Incremental Episodic Memory Adaptive Resonance Theory (In-EMART), consists two layers of ART networks which used to detect novel event encountered by the robot and learn the spatio-temporal relationship by creating neurons incrementally. A set of connected episodes forms a sensorimotor map that can be used for path planning and goal navigation autonomously. The experimental results for a mobile robot show that: (i) In-EMART can learn sensory data in real time which is important for robot implementation; (ii) the model solves the perceptual aliasing issue by recalling the connected episode neurons; (iii) compared with previous works, the proposed method further generates a sensorimotor map for connecting episodes together to navigate from one place to another continuously.
- リンク情報
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- DOI
- https://doi.org/10.1109/KCIC.2018.8628468
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000459878100052&DestApp=WOS_CPL
- Scopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85062863614&origin=inward
- Scopus Citedby
- https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85062863614&origin=inward
- ID情報
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- DOI : 10.1109/KCIC.2018.8628468
- ISBN : 9781538680797
- SCOPUS ID : 85062863614
- Web of Science ID : WOS:000459878100052