Wubing Fang
Papers
6
Total Citations
173
H-Index
6
About
Wubing Fang is a pioneering researcher in intelligent robotics and neural network control systems, with a focus on brain-inspired emotional learning mechanisms for autonomous robots. His major contributions lie in developing adaptive control systems that enhance robotic perception and manipulation, particularly through self-organizing neural networks that mimic biological emotional learning. Fang’s most cited work, “Self-Organizing Brain Emotional Learning Controller Network for Intelligent Control System of Mobile Robots” (2018, 51 citations), introduced a novel controller that significantly improves trajectory tracking under uncertain disturbances. He extended this concept to vision-based mobile robots with a recurrent emotional CMAC neural network (2019, 38 citations), and to robotic grasping with dual neural network controllers (2020, 33 citations). His improved fuzzy brain emotional learning model for humanoid robots (2019, 28 citations) addressed limitations in existing BEL systems. Notably, Fang has also explored creative robotics, applying deep reinforcement learning to Chinese calligraphy robots (2018, 15 citations), demonstrating how emotional learning models can enable artistic expression. With over 170 total citations, Fang’s work bridges biological inspiration and practical robotics, advancing intelligent control systems for real-world applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Visual-Guided Robotic Object Grasping Using Dual Neural Network Controllers33 citations · 2020
- 4
- 5Towards Deep Reinforcement Learning Based Chinese Calligraphy Robot15 citations · 2018
- 6Towards a Robotic Chinese Calligraphy Writing Framework8 citations · 2018