Shenglun Yi
Papers
2
Total Citations
9
H-Index
2
About
Shenglun Yi is advancing the frontiers of intelligent robotic control and human-robot interaction. His research focuses on two critical areas: high-precision adaptive control for robotic manipulators and intuitive gesture-based control for humanoid robots. In his most-cited work, "Adaptive Tunable Predefined-Time Backstepping Control for Uncertain Robotic Manipulators" (2024, 7 citations), Yi introduces a novel control strategy that ensures tracking convergence within a user-specified time frame, even under system uncertainties—a breakthrough for complex operational tasks requiring extreme precision. This work addresses a fundamental challenge in engineering applications where traditional control methods fall short. Complementing this, his study "Enhanced Recognition for Finger Gesture-Based Control in Humanoid Robots Using Inertial Sensors" (2024, 2 citations) explores how inertial sensors can provide more efficient and intuitive command inputs for remote operation of humanoid robots. By leveraging sensor data for gesture recognition, Yi’s work paves the way for more natural human-robot interfaces. Though early in his career, Yi’s contributions are already shaping the future of adaptive control and robotic teleoperation, demonstrating significant potential for real-world impact in manufacturing, healthcare, and beyond.
Research Focus
Key Achievements
Top Papers
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- 2