Wuji Liu
Papers
5
Total Citations
89
H-Index
4
About
Wuji Liu is a pioneering researcher in soft robotics, specializing in shape memory alloy (SMA)-driven systems and their intelligent control. His work bridges biomimetic design with reinforcement learning to create flexible manipulators capable of complex tasks like object grasping and target searching. Liu’s most impactful contribution, “Distance-directed Target Searching for a Deep Visual Servo SMA Driven Soft Robot Using Reinforcement Learning” (50 citations), demonstrates how deep learning can enable soft robots to autonomously navigate and interact with their environment. He further advanced the field with an inchworm-snake inspired flexible robotic manipulator (26 citations), which combines rigid and flexible materials to achieve both stability and compliant motion—a critical breakthrough for practical grasping applications. Liu also developed a continuous reinforcement learning algorithm for position control of SMA-driven soft robots, addressing long-standing challenges in precision and adaptability. His work on multi-segment manipulators and kinematic modeling has laid the groundwork for next-generation soft robots that are safer, more dexterous, and capable of operating in unstructured environments. With a growing citation impact, Liu is shaping the future of soft robotics for medical, industrial, and exploration applications.
Research Focus
Key Achievements
Top Papers
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