Max Robotics — Global Service Robot Directory + US Certification

About

Mun-Ho Jeong is a robotics and computer vision researcher whose work centers on humanoid robot systems, sensor calibration, and human-robot interaction. His contributions span robot perception, motion generation, and intelligent sensing, with a particular focus on bridging the gap between human behavior and robotic capability. Jeong's most influential work includes pioneering calibration methodologies for robot head-eye systems, developing the Minimum Variance approach to accurately estimate transformations between robot and camera coordinate frames — a foundational challenge in humanoid vision systems. This work, along with its stereo camera extension, has garnered over 34 combined citations. He has also made notable contributions to humanoid robot platforms, co-developing MAHRU-M, a mobile humanoid robot with a dual-network control architecture, and contributing to the broader MAHRU ubiquitous robotic companion project. In human-robot interaction, Jeong advanced imitation learning frameworks using Evolutionary Algorithms to generate natural human-like arm motions, and developed HMM-based systems for detecting visual speakingness and human attentiveness — critical capabilities for socially intelligent robots. His research has accumulated nearly 130 citations across a decade of published work, reflecting sustained influence in the humanoid robotics community and practical contributions to real-world robot deployment and perception systems.