Tonghai Hu
Papers
2
Total Citations
59
H-Index
2
About
Tonghai Hu is a leading researcher at the intersection of robotics, artificial intelligence, and smart infrastructure, whose work is pioneering the use of deep learning for precise robotic control in smart cities and industrial automation. His primary contributions lie in developing advanced computational models for manipulator inverse dynamics—a critical challenge for ensuring robot dexterity and safety. Hu’s most influential work, "Modeling and Simulation of Robot Inverse Dynamics Using LSTM-Based Deep Learning Algorithm for Smart Cities and Factories" (2019, 48 citations), introduced a novel LSTM-based approach that overcomes the limitations of traditional physical modeling, which struggles with unpredictable factors like joint flexibility and friction. Building on this, his "Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method" (2020, 11 citations) further refined these techniques, offering a hybrid solution that combines the interpretability of parametric models with the adaptability of deep learning. Together, these papers have established Hu as a key innovator in enabling high-accuracy, secure robotic operations for next-generation smart environments, directly impacting the reliability of autonomous systems in factories and urban settings.
Research Focus
Key Achievements
Top Papers
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