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About

Yu-Cheng Chen is a multidisciplinary researcher whose work bridges photonics, robotics, and artificial intelligence. His primary research areas include biological tunable photonics, multi-robot formation control, autonomous navigation, and reinforcement learning for robotic systems. Chen’s major contributions span both fundamental and applied domains. In photonics, he pioneered the concept of “biological tunable photonics,” exploring how living biomaterials can dynamically control light–matter interactions for next-generation optoelectronic sensors and devices—a work that has garnered 18 citations since 2021. In robotics, he developed obstacle avoidance and role assignment algorithms for formation control of multiple mobile robots (17 citations, 2007) and designed dynamic object-tracking control laws for non-holonomic wheeled autonomous robots using Lyapunov’s direct method and computed-torque control (14 citations, 2009). Most recently, Chen has advanced reinforcement learning for real-world robotic control by introducing gradient-based regularization to ensure action smoothness, addressing a critical challenge in deploying DRL in dynamic environments. His work demonstrates a unique ability to integrate insights from biology, optics, control theory, and machine learning, making him a notable figure in the emerging field of bio-inspired intelligent systems.