Yanbin Gao
Papers
11
Total Citations
198
H-Index
7
About
Yanbin Gao is a robotics and artificial intelligence researcher whose work spans autonomous navigation, computer vision, reinforcement learning, and multi-sensor fusion. He is best known for his contributions to intelligent robotic systems, particularly in developing practical, low-cost solutions for real-world deployment. His 2019 paper on indoor navigation for food delivery robots — combining multi-sensor information fusion to address labor shortages in the restaurant industry — has garnered 61 citations, establishing him as a notable voice in service robotics. Gao has made significant strides in learning-based robot navigation, proposing hybrid frameworks that integrate global and local planners, unsupervised learning, and biologically inspired hierarchical reinforcement learning drawing on hippocampal models of spatial cognition. His Actor-Dueling-Critic method advances model-free reinforcement learning for robotic control, while his image object extraction work leverages YOLOv3 with improved clustering algorithms. Beyond mobile robotics, Gao has contributed to visual servoing for robotic manipulators and pipeline inspection gauge navigation using micro-inertial sensing. With over 190 cumulative citations across a focused body of work, his research consistently bridges theoretical machine learning with applied robotic engineering challenges.
Research Focus
Key Achievements
Top Papers
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- 5The Actor-Dueling-Critic Method for Reinforcement Learning21 citations · 2019
- 6Enhanced IBVS controller for a 6DOF manipulator using hybrid PD-SMC method11 citations · 2017
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- 8Junction Detection Based on CCWT and MEMS Accelerometer Measurement4 citations · 2018
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