Hongrui Zheng
Papers
4
Total Citations
90
H-Index
4
About
Hongrui Zheng is a robotics researcher whose work bridges autonomous systems, human-robot interaction, and machine learning. His key research areas include miniature aerial robotics, deep reinforcement learning (DRL), and implicit neural representations for localization. Zheng’s most impactful contribution is the development of the Georgia Tech Miniature Autonomous Blimp (GT-MAB), which achieved the first-ever human-robot interaction demonstration between an uninstrumented human and a robotic blimp. This work, published in 2017 and garnering 77 citations, introduced a monocular vision-based human-following approach that opened new possibilities for safe, indoor HRI missions. More recently, Zheng has tackled the simulation-to-reality gap in DRL by proposing an online supervisor-based training method (2023), allowing robots to learn control policies directly from real-world experience without relying on simulated environments. He has also advanced robot localization with Local_INN (2023), a framework using Invertible Neural Networks to solve ambiguous pose estimation problems. Beyond research, Zheng is dedicated to robotics education, co-authoring a 2022 paper on leveraging modular small-scale hardware to give students hands-on experience with autonomous systems, helping bridge the gap between theory and real-world deployment in the classroom.
Research Focus
Key Achievements
Top Papers
- 1Monocular vision-based human following on miniature robotic blimp77 citations · 2017
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