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About

Jangho Bae is a leading researcher in human-robot collaboration (HRC) and construction automation, with a career dedicated to making robots safer and more intuitive partners for human workers. His most influential work, "Variable Admittance Control With Virtual Stiffness Guidance for Human–Robot Collaboration" (2020, 37 citations), introduced a novel control framework that dynamically adjusts robot compliance, enabling smoother and more precise cooperation in unstructured industrial environments. Bae’s contributions extend to heavy machinery automation, where his 2013 paper on "Contour control for leveling work with robotic excavator" (36 citations) pioneered precision control for earthmoving tasks. He has also advanced wearable robotics with his 2015 study on "Ceiling work scenario based hardware design and control algorithm of supernumerary robotic limbs" (23 citations), addressing aging workforce challenges in construction. Earlier work on "Optimal path generation for excavator with neural networks based soil models" (2008, 15 citations) integrated machine learning to optimize excavation efficiency. Across his career, Bae’s research has consistently bridged control theory and practical robotics, with his variable impedance and admittance control methods forming the foundation for safer, more adaptive human-robot cooperation systems.