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About

Manjeevan Seera is a leading researcher at the intersection of cognitive robotics, affective computing, and human-robot interaction. His work focuses on endowing robots with human-like memory and emotional architectures to enable more natural, adaptive interactions. Seera’s major contributions include developing personality-affected robotic emotional models that integrate associative memory, allowing robots to exhibit mood-congruent behaviors during interactions with humans. His most cited work, "Personality affected robotic emotional model with associative memory for human-robot interaction" (49 citations), demonstrates how robots can modulate emotional responses based on personality traits, enhancing social engagement. In a notable achievement, Seera proposed the Enhanced Episodic Memory Adaptive Resonance Theory (EEM-ART) model, an unsupervised learning framework that enables robots to build cognitive maps from sensorimotor experiences for navigation and memory recall (15 citations). He has also advanced multi-channel Bayesian adaptive resonance architectures for topological map building (14 citations) and hybrid evolutionary neuro-fuzzy systems for gesture recognition (12 citations). With a cumulative impact exceeding 100 citations, Seera’s work bridges artificial intelligence and psychology, paving the way for emotionally intelligent robots capable of learning from and adapting to their environments.