Yi‐Chang Li

Papers

Total Citations

H-Index

About

Yi-Chang Li is a rising force in robotics and control theory, whose work bridges data-driven modeling and optimal control for complex robotic systems. His primary research areas include Koopman operator theory, nonlinear control, and reinforcement learning for robotics. Li’s most significant contribution is the development of the Kalman–Koopman linear quadratic regulator (KKLQR) control approach, a novel framework that leverages neural networks to construct continuous Koopman eigenfunctions without relying on predefined dictionaries. This breakthrough, published in 2024 and already garnering 25 citations, enables efficient linearization of nonlinear robotic dynamics, allowing for real-time optimal control in high-dimensional systems. By integrating Kalman filtering with Koopman theory, Li’s work addresses critical challenges in state estimation and control robustness, offering a scalable alternative to traditional model-based methods. His approach has immediate applications in autonomous manipulation, legged locomotion, and human-robot interaction. With a citation trajectory that signals growing influence, Li is establishing himself as a key innovator at the intersection of machine learning and control, pushing the boundaries of how robots learn and adapt in unstructured environments.