Papers
1
Total Citations
2
H-Index
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About
Guanghao Su is a pioneering researcher at the intersection of robotics, control theory, and reinforcement learning. His primary research areas include flexible robot control, inverse reinforcement learning, and multi-time scale dynamic systems. Su's most notable contribution is the development of a two-time scale primal-dual inverse reinforcement learning framework for flexible robots, which addresses the critical challenge of tracking control under reference signal loss and modeling errors—a problem that has long hindered the practical deployment of lightweight, high-speed robotic arms. This work, published in 2024 and already garnering 2 citations, demonstrates his ability to tackle complex, real-world control problems by integrating advanced learning algorithms with classical control theory. By enabling flexible robots to maintain precise tracking despite vibrations and uncertainties, Su's research has significant implications for manufacturing, surgical robotics, and space exploration. His innovative approach to combining primal-dual optimization with inverse reinforcement learning marks him as an emerging leader in adaptive robotic control, with the potential to reshape how we design and operate next-generation, high-performance robotic systems.
Research Focus
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Top Papers
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