Papers
5
Total Citations
174
H-Index
4
About
Zhian Kuang is a robotics researcher whose work sits at the intersection of control theory, reinforcement learning, and human-robot interaction. His primary research areas include variable impedance control, sliding mode control, and multi-robot task allocation. Kuang’s most influential contribution is his 2021 paper on learning variable impedance control via inverse reinforcement learning for force-related tasks, which has garnered 112 citations. This work addresses a critical challenge in robotics: enabling robots to safely and adaptively interact with unknown environments by learning impedance parameters from demonstrations. He has also made significant advances in deploying tethered space robots through fuzzy approximate learning-based sliding mode control (40 citations), and has tackled the combinatorial complexity of multi-robot task allocation with limited spans using hybrid genetic algorithms. Kuang’s research is characterized by a practical, safety-conscious approach—exemplified by his work on safe online gain optimization for Cartesian space impedance control. His contributions are particularly relevant for applications requiring precise, adaptive force control, from industrial manipulation to space robotics.
Research Focus
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Top Papers
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