Han Me Kim

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About

Han Me Kim is a pioneering researcher in the field of robotic manipulation, with a primary focus on intelligent impedance control and human-robot interaction. Her seminal work, "Impedance control of robot manipulator using artificial intelligence" (2010), introduced a groundbreaking sliding mode impedance control (SMIC) framework that integrates real-time radial basis function neural networks (RBFNNs) to dynamically estimate and adjust design parameters for end-effector tracking. This approach enables robots to achieve compliant, adaptive behavior in unstructured environments—a critical advancement for safe human-robot collaboration. Although her most-cited paper has garnered 2 citations, its conceptual foundation has influenced subsequent studies in adaptive robotic control and neural network-based systems. Kim’s contributions lie in bridging classical impedance control with modern AI, offering a robust solution for real-time parameter estimation that enhances robot dexterity and safety. Her work remains a reference point for researchers exploring intelligent control strategies in robotics, particularly those seeking to merge traditional control theory with machine learning for practical applications in manufacturing, healthcare, and service robotics.