An incremental episodic memory framework for topological map building

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.