Weiyi Ying
Papers
2
Total Citations
15
H-Index
2
About
Weiyi Ying is a rising researcher in the field of robotics, with a focused expertise in impedance control and human-robot interaction. Their work centers on developing efficient, data-driven methods for tuning robotic controllers to achieve precise and safe physical interactions with the environment. Ying’s major contributions lie in the application of multi-objective Bayesian optimization to impedance control, a critical area for tasks requiring delicate force modulation, such as assembly or assistive robotics. Their most cited work (2023, 12 citations) pioneered a Bayesian optimization-based approach for efficient impedance controller tuning using force feedback, significantly reducing the manual effort traditionally required. Building on this, their 2024 paper (3 citations) advanced the state-of-the-art by simultaneously optimizing for both transient and steady-state performance, moving beyond single-objective methods to achieve more robust force tracking. While early in their career, Ying’s work is notable for its practical impact, directly addressing a key bottleneck in deploying adaptive robots in unstructured environments. Their research is essential reading for students and engineers seeking to implement intelligent, self-tuning control systems for real-world robotic interaction.
Research Focus
Key Achievements
Top Papers
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