Papers
20
Total Citations
419
H-Index
11
About
Xiaojie Su is a prominent robotics and control systems researcher whose work spans advanced manipulation control, sliding mode control theory, and reinforcement learning for robotic systems. His research addresses some of the most challenging problems in robotics: enabling machines to operate reliably in uncertain, real-world environments. Su's most influential contributions lie in adaptive and hybrid control strategies for robotic manipulators. His work on adaptive hybrid impedance control for dual-arm cooperative manipulation (2022, 77 citations) and adaptive fuzzy-based force/position control (2021, 68 citations) has significantly advanced how robots handle physical interactions with uncertain objects. His foundational contributions to sliding mode control of hybrid systems (2014, 60 citations) remain widely cited across the control theory community. More recently, Su has pioneered the application of reinforcement learning to complex robotic manipulation, developing graph-based approaches to overcome sparse reward challenges (2022, 44 citations), and has embraced cutting-edge AI by integrating large language models and knowledge graphs to improve robot intention prediction (2024). His explorations of language-conditioned imitation learning further demonstrate a forward-looking research agenda bridging classical control with modern AI. With over 350 cumulative citations, Su's work continues shaping the future of intelligent, adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3Sliding Mode Control of Uncertain Parameter‐Switching Hybrid Systems60 citations · 2014
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- 6Complex Robotic Manipulation via Graph-Based Hindsight Goal Generation33 citations · 2021
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