Yunxiao Ren

University of Science and Technology Beijing, Peking University

Papers

Total Citations

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About

Yunxiao Ren’s research bridges two exciting frontiers: intelligent soft robotics and advanced control theory for uncertain systems. Ren’s most impactful work introduces a healable, multi-driving-mode soft actuator using disulfide-bonded liquid crystal elastomers (2024, 27 citations), a breakthrough enabling self-repairing, flexible robots for applications in wearable devices and biomedical tools. This innovation demonstrates how molecular design can yield materials that autonomously recover from damage while offering versatile actuation modes. In parallel, Ren tackles fundamental challenges in robot manipulation through Gaussian process-based control. Their 2025 study on high-accuracy tracking control for uncertain robot manipulators (3 citations) pioneers a sparse online Gaussian process approach, allowing adaptive feedback gains without requiring explicit system models. Earlier foundational work (2021, 2 citations) established Gaussian process frameworks for modeling unknown robotic dynamics, enabling robust tracking despite dynamical uncertainties. Ren’s contributions are particularly notable for merging data-driven machine learning with traditional control—a paradigm shift that promises more resilient, adaptive robots. By addressing both material-level soft robotics and algorithmic control, Ren’s work offers a comprehensive vision for next-generation autonomous systems capable of operating safely in unpredictable environments.