Bing Hao
Papers
2
Total Citations
59
H-Index
2
About
Bing Hao is a researcher specializing in robotics, machine learning, and intelligent systems, with a particular focus on advancing robotic applications for smart cities and industrial environments. His work addresses a fundamental challenge in robotics: the limitations of traditional physical modeling approaches, which struggle to account for uncertain and nonlinear factors such as joint clearance, friction, and structural flexibility in robotic manipulators. Hao's most notable contribution is his development of deep learning-based methods for robot inverse dynamics modeling. His 2019 paper introduced an LSTM-based deep learning algorithm for this purpose, earning 48 citations and establishing him as a pioneer in applying recurrent neural network architectures to robotic control problems. Building on this foundation, his 2020 work proposed a semiparametric deep learning approach that combines the strengths of physical modeling with data-driven techniques, offering improved accuracy and adaptability for real-world smart city and industrial deployments. Together, these contributions reflect Hao's commitment to bridging the gap between theoretical robotics and practical implementation, making autonomous systems safer and more precise. His research holds meaningful implications for the growing integration of intelligent robotics across manufacturing, urban infrastructure, and beyond.
Research Focus
Key Achievements
Top Papers
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- 2