This survey samples from the ever-growing family of adaptive resonance theory (ART)
neural network models used to perform the three primary machine learning modalities …

For social human robot collaboration, robots need to effectively remember human
experiences and manage emotional experiences as well as repetitive experiences. To …

This paper presents a robotic behavior planning method under uncertainty based on biology-
inspired episodic memory. Adaptive behavior planning, prediction and reasoning are …

Autonomous robots cohabiting with humans will have to achieve recurring tasks while
adapting to the changing conditions of the world. A spatio-temporal memory categorizes the …

Companion robots will be more and more part of our daily lives in the next years, and having
long-term interactions can have both positive and negative effects on their users. This paper …

Inspired by mammal's spatial awareness and navigation capabilities, a new episodic-
cognitive map building and path planning method was proposed, used for navigation tasks …

Social robots are expected to be part of everyday life of people. This will generate
interactions between humans and robots that may have positive or negative effects on the …

A common goal of robot control architecture designers is to create systems that are
sufficiently generic to be adapted to different robot hardware. Beyond code re-use from a …

Abstract Adaptive Resonance Theory (ART) has emerged as a significant framework in the
realm of image processing, particularly in object identification and recognition. This review …

Purpose This paper aims to focus on the autonomous behavior selection issue of robotics
from the perspective of episodic memory in cognitive neuroscience with biology-inspired …