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About

Sisi Liu is a leading researcher in the fields of human-robot interaction, adaptive control, and intelligent robotics. Her work focuses on enabling seamless collaboration between humans and robots, particularly in physically interactive tasks. Liu’s major contributions include developing an adaptive neural network force tracking control for flexible joint robots operating in uncertain environments, which ensures precise force regulation during contact tasks—a critical advancement for industrial and assistive robotics. She also pioneered a hybrid visual-haptic framework that synchronizes motion in human-robot cotransporting, addressing real-time human motion prediction to overcome communication delays. Her research on improved biological neural networks for path planning of agricultural robots demonstrates her versatility in applying bio-inspired algorithms to real-world automation. With over 80 citations across her most-cited works, Liu’s impact is evident in her ability to bridge theoretical control methods with practical robotic applications. Her notable achievements include the development of long short-term memory (LSTM)-based human motion prediction for co-carrying tasks, enabling robots to anticipate and lead collaborative actions. Liu’s work is essential reading for students and researchers interested in adaptive control, human-robot collaboration, and intelligent motion planning.