Ge Gao

University of Bremen, Southeast University, Ministry of Education of the People's Republic of China

Papers

Total Citations

H-Index

About

Ge Gao is a robotics researcher whose work sits at the intersection of human-robot interaction, sensor fusion, and robot learning. His research focuses on enabling service robots to perceive, understand, and respond to human behavior in dynamic real-world environments — a challenge central to the next generation of intelligent autonomous systems. Among his notable contributions is a robust low-level sensor fusion framework for human tracking, combining stereo camera depth data with infrared 2D laser range finder inputs to allow mobile robots to follow humans with high reliability and responsiveness (2016, 5 citations). Complementing this, Gao has advanced activity recognition for service robots using RGB-D data, leveraging Gaussian Mixture Models and FastDTW to interpret complex human gestures in real time (2016, 3 citations). His more recent work explores Learning by Demonstration, addressing the particularly difficult problem of teaching robots under-specified object manipulation tasks by modeling spatial-temporal relationships between sub-activities and object affordances (2018, 2 citations). Though his citation counts reflect an early-career trajectory, Gao's research tackles foundational problems in human-aware robotics — work that is increasingly vital as service robots move from controlled labs into everyday human environments.