Weizhen Liu
Papers
2
Total Citations
9
H-Index
2
About
Weizhen Liu is a researcher focused on advancing human-robot interaction, with a particular emphasis on enhancing the precision and safety of teleoperated robotic systems. His primary research areas include physiological tremor attenuation, broad learning systems, and convolutional neural network-based filtering for telerobotics. Liu’s major contributions lie in developing innovative tremor attenuation filters that significantly improve control accuracy in master-slave teleoperated robots. His most cited work, "A Convolutional Neural Network-Based Broad Incremental Learning Filter for Attenuating Physiological Tremors in Telerobot Systems" (2023, 5 citations), introduces a novel approach to counter the destabilizing effects of involuntary hand tremors during remote operation. Building on this, his earlier study, "Tremor Attenuation For Robot Teleoperation By A Broad Learning System-Based Approach" (2021, 4 citations), established a foundational framework for eliminating physiological tremors to achieve seamless synchronization between human operators and robotic manipulators. Though his citation counts are modest, Liu’s work is notable for its practical impact on real-world applications, such as minimally invasive surgery and hazardous environment exploration, where even minor tremors can compromise system stability. His research represents a critical step toward more reliable and intuitive teleoperation technologies.
Research Focus
Key Achievements
Top Papers
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