Max Robotics — Global Service Robot Directory + US Certification

About

Martin Huber is a leading researcher in surgical robotics and embodied AI, with a focus on advancing minimally invasive surgery (MIS) through intelligent automation. His work centers on visual servoing, deep reinforcement learning, and multimodal sensing to enhance intraoperative precision and autonomy. Huber’s major contributions include pioneering homography-based visual servoing with remote center of motion for semi-autonomous robotic endoscope manipulation, a method that departs from traditional point-based approaches to improve field-of-view control in MIS. He also developed the LBR-Stack, a ROS 2 and Python integration for KUKA’s Fast Robot Interface, enabling hard real-time applications for medical and industrial robots. His deep reinforcement learning system for intraoperative hyperspectral video autofocusing addresses critical hardware limitations in real-time tissue differentiation. Huber’s impact is reflected in his most-cited papers, including his 2021 work on visual servoing (9 citations) and the LBR-Stack (8 citations). He also leads the MUTUAL project, a cross-platform multimodal data platform for holistic sensing in the operating room, and explores deep homography prediction for endoscopic camera motion imitation learning. Huber’s work is shaping the future of intelligent surgical robotics and embodied AI in clinical settings.