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About

Hosun Kang is a robotics researcher whose work centers on robot perception, navigation, and locomotion, with a particular focus on enabling robots to operate effectively in complex, real-world environments. Kang’s major contributions span object manipulation, precise indoor navigation, and stable legged locomotion. In their most cited work, "Mask-RCNN based object segmentation and distance measurement for Robot grasping" (2019, 7 citations), Kang proposed a novel application of Mask-RCNN for accurate object segmentation from cluttered backgrounds, directly improving robotic grasping capabilities. For navigation, Kang developed a "Modified ORB-SLAM Algorithm for Precise Indoor Navigation of a Mobile Robot" (2020, 3 citations), enhancing visual SLAM accuracy for mobile robots. In legged robotics, Kang’s "LIPCPM: A Novel Model for Anti-Sloshing and Stable Bipedal Robot Locomotion" (2025, 2 citations) introduces the Linear Inverted Pendulum with Cart-Plate Model, a pioneering framework that simultaneously controls robot balance and liquid sloshing—a critical challenge for robots transporting fluids. This work, alongside research on quadruped robot stability (2023, 2 citations), demonstrates Kang’s commitment to solving practical, interdisciplinary problems in robotics, making their contributions valuable for advancing autonomous systems in dynamic settings.