Papers

Peer-reviewed
2019

Episodic Memory Multimodal Learning for Robot Sensorimotor Map Building and Navigation.

IEEE Trans. Cogn. Dev. Syst.
  • Wei Hong Chin
  • ,
  • Yuichiro Toda
  • ,
  • Naoyuki Kubota
  • ,
  • Chu Kiong Loo
  • ,
  • Manjeevan Seera

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
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

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