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About

Lilan Liu is a leading researcher in human-robot collaboration, mobile robotics, and intelligent manufacturing, with a focus on creating intuitive and efficient systems for real-world applications. Her work bridges the gap between human cognition and robotic execution, as demonstrated by her highly cited paper on a novel mobile robot navigation method using hand-drawn paths (13 citations), which simplifies human-robot interaction by leveraging instinctive human planning. Liu’s major contributions include developing a dynamic task allocation framework for human-robot collaborative assembly, integrating digital twin technology with an improved genetic algorithm and tabu search (IGA-TS) to optimize real-time task distribution (11 citations). She also advanced action recognition in collaborative tasks through hybrid convolutional neural networks, enhancing assembly quality and efficiency in sustainable manufacturing (11 citations). Her research extends to motion planning for manipulators using deep reinforcement learning (DDPG) and evaluating task allocation plans for human-robot teams, with notable work on master manipulators for vascular interventional surgery, improving training for clinical procedures. With a growing citation impact and a focus on practical, scalable solutions, Liu is shaping the future of human-robot collaboration in both industrial and healthcare settings.