Kuan‐Ching Li
Papers
2
Total Citations
29
H-Index
2
About
Kuan-Ching Li is a leading researcher at the intersection of robotics control and computer vision, whose work bridges adaptive physical interaction with intelligent scene understanding. His major contributions center on developing robust control strategies for robots operating in highly dynamic, unknown environments—a critical challenge for industrial automation. In his highly cited 2023 paper on adaptive fractional-order admittance control, Li introduced a novel scheme that enables robots to maintain precise force tracking during contact with unpredictable surroundings, achieving 17 citations for its practical impact on manufacturing and human-robot collaboration. Expanding into perception, Li’s work on MonoSAID tackles monocular 3D object detection by proposing scene-level adaptive instance depth estimation, a method that significantly improves depth accuracy from single-camera inputs, garnering 12 citations for its potential in autonomous driving and robotics. His research is notable for integrating fractional calculus with control theory, offering a mathematically rigorous yet deployable solution to real-world interaction problems. With a growing citation record and a focus on high-impact applications, Li is establishing himself as a key contributor to next-generation robotic systems that must both sense and act in complex, unstructured environments.
Research Focus
Key Achievements
Top Papers
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