Episodic Memory Multimodal Learning for Robot Sensorimotor Map Building and Navigation

Wei Hong Chin, Yuichiro Toda, Naoyuki Kubota, Chu Kiong Loo, Manjeevan Seera

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Article number8488558
Pages (from-to)210-220
Number of pages11
JournalIEEE Transactions on Cognitive and Developmental Systems
Issue number2
Publication statusPublished - Jun 2019
Externally publishedYes


  • Adaptive resonance theory (ART)
  • episodic memory
  • robot navigation

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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