Leon Yan

Papers

Total Citations

H-Index

About

Leon Yan is a rising researcher at the forefront of precision robotics and intelligent manufacturing, with a core focus on iterative learning control (ILC), data-driven robot learning, and human-robot interaction with deformable materials. His work bridges the gap between advanced control theory and real-world industrial applications, particularly in tasks requiring high accuracy and adaptability. Yan’s most cited paper, "MIMO ILC using complex-kernel regression and application to Precision SEA robots" (2021, 8 citations), introduces a novel multi-input multi-output learning framework that significantly enhances the precision of series elastic actuator robots—a key contribution to safe, compliant automation. More recently, his 2024 paper, "Active Data-Enabled Robot Learning of Elastic Workpiece Interactions," tackles the critical challenge of maintaining tool-workpiece normality during elastic structure manufacturing, such as clamping and drilling, to prevent damage. By proposing a model-free, data-driven approach, Yan reduces reliance on complex physics-based models, enabling robots to learn and adapt to variable workpiece interactions in real time. Though early in his career, his work is already shaping next-generation adaptive manufacturing systems, with growing citations reflecting its practical impact.