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About

Naijun Liu is a leading researcher in robot learning and intelligent control, with a focus on bridging the gap between simulation and real-world deployment. His work centers on deep reinforcement learning (DRL) and digital twin technologies to enable complex, real-world robotic manipulation. Liu’s major contributions include pioneering the “Real–Sim–Real” transfer framework, which allows control policies trained in simulation to be effectively deployed on physical robots—a breakthrough that reduces the cost and risk of real-world training. His research on digital-twin-assisted skill learning has been instrumental in advancing automation for 3C (computer, communication, and consumer electronics) assembly, particularly for intricate tasks like flexible printed circuit assembly. With over 60 citations across his most-cited works, Liu’s impact is evident in both academic and industrial settings. Notably, his 2020 paper on real-world robot control policy learning has garnered 29 citations, reflecting its influence in the field. Liu’s work also extends to virtual reality teleoperation, enhancing human-robot interaction for hazardous environments. His achievements underscore a commitment to making robots more adaptable and intelligent for unstructured, real-world applications.