Zixuan Huo
Papers
4
Total Citations
15
H-Index
3
About
Zixuan Huo is an emerging robotics researcher whose work spans reinforcement learning-based decision-making and advanced force control for robotic manipulators. His early research tackled the classic pursuit-evasion problem in mobile robotics, introducing a hierarchical reinforcement learning framework that enables autonomous agents to deploy multiple strategies when tracking moving targets — work that has garnered 6 citations since its 2020 publication. More recently, Huo has focused on a particularly challenging frontier in robotics: achieving precise interaction force control in uncertain, dynamically changing environments. His contributions include an observer-based adaptive robust control system that integrates external force/torque sensing to maintain accurate force modulation even when environmental stiffness and contact location are unknown or variable. Complementing this, his adaptive robust interaction force control framework introduces novel environment interaction modeling that captures both structured and unstructured uncertainties. His most recent work extends these themes further by addressing real-time disturbances in integrated planning and control pipelines. Collectively accumulating 15 citations across a focused body of work, Huo demonstrates a clear trajectory toward establishing expertise in intelligent, resilient robotic control systems for real-world deployment.
Research Focus
Key Achievements
Top Papers
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