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About

Lingyue Kong is a rising researcher in robotics and autonomous systems, focusing on the critical challenges of control, navigation, and safety in dynamic, uncertain environments. Their work bridges theoretical control theory and practical machine learning to enhance robot performance. A major contribution is the development of a time delay estimation (TDE)-based adaptive super-twisting sliding mode control for cable-driven manipulators, which guarantees high-precision trajectory tracking while enforcing safety constraints on tracking errors—a vital achievement for human-robot interaction. This work has garnered 4 citations. Kong also addresses the fundamental problem of mobile robot navigation in crowded, unpredictable spaces. By integrating trajectory prediction with reinforcement learning, they have created a robust framework for online path planning, outperforming traditional replanning methods. Furthermore, their research on cross-modal fusion and knowledge transfer directly tackles the persistent "sim-to-real" gap, enabling robots trained in simulation to generalize effectively to real-world environments. With a growing citation record, Kong’s work is establishing a strong foundation for more intelligent, safe, and adaptable robotic systems.