2019
Episodic Memory Multimodal Learning for Robot Sensorimotor Map Building and Navigation.
IEEE Trans. Cogn. Dev. Syst.
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- Volume
- 11
- Number
- 2
- First page
- 210
- Last page
- 220
- Language
- English
- Publishing type
- Research paper (scientific journal)
- DOI
- 10.1109/TCDS.2018.2875309
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
In this paper, an unsupervised learning model of episodic memory is proposed. The proposed model, enhanced episodic memory adaptive resonance theory (EEM-ART), categorizes and encodes experiences of a robot to the environment and generates a cognitive map. EEM-ART consists of multilayer ART networks to extract novel events and encode spatio-temporal connection as episodes by incrementally generating cognitive neurons. The model connects episodes to construct a sensorimotor map for the robot to continuously perform path planning and goal navigation. Experimental results for a mobile robot indicate that EEM-ART can process multiple sensory sources for learning events and encoding episodes simultaneously. The model overcomes perceptual aliasing and robot localization by recalling the encoded episodes with a new anticipation function and generates sensorimotor map to connect episodes together to execute tasks continuously with little to no human intervention.
- Link information
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- DOI
- https://doi.org/10.1109/TCDS.2018.2875309
- DBLP
- https://dblp.uni-trier.de/rec/journals/tamd/ChinTKLS19
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000471119200007&DestApp=WOS_CPL
- URL
- https://dblp.uni-trier.de/db/journals/tamd/tamd11.html#ChinTKLS19
- ID information
-
- DOI : 10.1109/TCDS.2018.2875309
- ISSN : 2379-8920
- eISSN : 2379-8939
- DBLP ID : journals/tamd/ChinTKLS19
- Web of Science ID : WOS:000471119200007