Max Robotics — Global Service Robot Directory + US Certification

About

Sang-Yong Rhee’s research career bridges classic robotics and modern deep learning, with a focus on making mobile robots more intuitive and autonomous. His early work pioneered vision-based navigation for mobile robots using a single camera and guide marks, laying groundwork for path-planning systems. He later advanced human-robot interaction by developing natural hand gesture recognition systems based on Hidden Markov Models (HMM) and fuzzy inference, enabling robots to understand spontaneous commands without requiring users to memorize predefined gestures. In the realm of precision agriculture, Rhee introduced ATT-UNet, a pixel-wise staircase attention mechanism for weed and crop segmentation, addressing the challenge of reducing herbicide overuse through targeted deep learning. His contributions also extend to mechanical design, including an inverted pendulum-based platform for upright-running mobile robots. With over 40 citations across his most-cited works, Rhee’s impact is seen in both foundational robotics and applied AI. His 2023 ATT-UNet paper, already garnering 8 citations, reflects his continued relevance in agricultural automation. For students, Rhee exemplifies how classic control theory and cutting-edge machine learning can converge to solve real-world problems—from guiding a robot through a corridor to identifying weeds in a field.