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About

Hang-Bong Kang is a pioneering researcher in human-robot interaction, focusing on how machines can naturally perceive and respond to human emotional and physical cues. His work bridges robotics, computer vision, and adaptive control, with key contributions in emotional communication, face pose recognition, and autonomous navigation. Kang’s most cited paper, “Emotional Interaction with a Robot Using Facial Expressions, Face Pose and Hand Gestures” (2012, 7 citations), introduces a multimodal framework that enables robots to interpret human emotions through facial expressions, head orientation, and gestures—a foundational step toward empathetic robotics. His earlier work, “A Robust Adaptive Controller for Rigid Robots” (2003, 6 citations), advances control theory by integrating gain dynamics with Riccati equations to enhance robot stability and performance. In “Human Robot Interaction using Face Pose Recognition” (2007, 5 citations), Kang leverages manifold learning for robust face pose estimation, enabling intuitive robot control. His more recent “Modified Sequential Monte Carlo Bayesian Occupancy Filter” (2015, 4 citations) improves grid mapping for autonomous driving and robotics. With a career spanning emotional AI, adaptive control, and perception systems, Kang’s research has shaped how robots interact with humans in dynamic environments, earning recognition for its interdisciplinary impact.