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About

Alexandros Giagkos is a robotics researcher whose work sits at the intersection of developmental robotics, embodied cognition, and sensorimotor learning. Drawing inspiration from human infant development, his research explores how robots — particularly humanoid platforms like the iCub — can acquire perceptual and cognitive capabilities through biologically grounded developmental processes. His most cited work, "Robot Multimodal Object Perception and Recognition" (2020, 13 citations), exemplifies his signature approach: modeling the physical and cognitive maturation stages of infancy to guide robotic learning of object perception across multiple sensory modalities. Complementary studies on gaze control, visual feature perception, and hierarchical schema formation further demonstrate his commitment to understanding how developmental constraints shape intelligent behavior. Notably, his investigation of play-based learning — from practice play through schema-chain discovery — reflects a sophisticated understanding of how exploratory behavior drives cognitive growth in both humans and machines. With contributions spanning from early reaching models implemented on humanoid robots to structured action-sequence discovery, Giagkos has carved out a distinctive niche that bridges cognitive science and autonomous robotics, offering valuable frameworks for researchers seeking biologically plausible pathways to machine intelligence.