Yunfei Ge

Dalian University of Technology, Beijing Academy of Artificial Intelligence

Papers

Total Citations

H-Index

About

Yunfei Ge is a pioneering researcher at the intersection of robotic perception and locomotion, whose work pushes the boundaries of how machines interact with the physical world. Ge’s primary research areas encompass active perception systems and dynamic whole-body control for humanoid robots. In a landmark 2021 study, Ge introduced an ensemble learning method for robot electronic noses, enabling active olfactory perception—a breakthrough that allows robots to intelligently sample and identify odors through directed sensor movement rather than passive diffusion. This work, with 10 citations, addresses critical challenges in miniaturization and low-power operation for robotic olfaction. More recently, in 2025, Ge tackled one of robotics’ most formidable challenges: humanoid locomotion on narrow terrain. By integrating dynamic balance mechanisms with reinforcement learning, Ge’s algorithm enables bipedal robots to traverse extreme environments—such as balance beams and narrow ledges—that have historically stymied even advanced locomotion systems. This work, already garnering 3 citations, demonstrates how biological principles of human balance can be translated into robust robotic control. Ge’s contributions are shaping the next generation of agile, perceptive robots capable of operating in complex, unstructured environments.