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About

Kui Hu is a robotics researcher whose work centers on intelligent manipulation, human-robot interaction, and real-time perception systems. His most significant contribution is in contact force detection and control for robotic polishing, where he developed a method using joint torque sensors to enable precise force regulation during surface finishing tasks—a paper that has garnered 59 citations and is widely referenced in manufacturing automation. Hu also advanced computer vision with a real-time night face detector that employs histogram equalization to enhance low-light images, achieving practical detection in unconstrained environments. In the domain of learning from demonstration, he proposed a kernelized gradient descent approach that allows robots to efficiently acquire skills from human examples, while his smooth time-optimal path tracking algorithm addresses the challenge of discontinuous control trajectories in robot manipulators. Additionally, Hu designed a novel robot finger with changeable surfaces for in-hand manipulation, aiming to simplify control in co-fusion robotic systems. His work on analytical inverse kinematics for 7-DOF anthropomorphic manipulators further demonstrates his commitment to making robotic learning more accessible and efficient. With a growing citation record and contributions spanning perception, control, and learning, Kui Hu is establishing himself as a versatile researcher in modern robotics.