Minho Lee
Papers
12
Total Citations
142
H-Index
9
About
Minho Lee is a pioneering researcher in neuro-robotics and developmental artificial intelligence, focusing on how autonomous agents can learn through the integration of perception, action, and cognition. His major contributions center on biologically inspired learning frameworks that enable robots to autonomously develop visual attention, depth estimation, and motor skills through cyclic perception-action loops—mimicking human infant learning. Notably, his work on proactive perception and action cyclic learning (2018) demonstrates how humanoid robots can enhance binocular depth estimation accuracy through sensorimotor interaction, a foundational concept for autonomous developmental robotics. Lee’s research also explores emotion recognition in natural scenes using brain activity patterns (2008) and the integration of language with action in neuro-robotics experiments (2010). With over 130 citations across his most-cited papers, his studies on selective attention models (2005) and integrative learning of visual attention shifts with bimanual object manipulation (2010) have significantly influenced the field of autonomous mental development. Lee’s work bridges neuroscience, computer vision, and robotics, offering critical insights for creating truly autonomous, learning-driven artificial agents.
Research Focus
Key Achievements
Top Papers
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- 4Neural Information Processing12 citations · 2013
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