Papers
3
Total Citations
111
H-Index
3
About
Samuel Lee is a leading researcher in soft robotics, with a focus on developing intelligent, adaptive systems for automation and rehabilitation. His work centers on three key areas: 3D-printed soft grippers, variable stiffness actuators, and biomimetic artificial muscles. Lee’s most notable contribution is his 2023 paper on a 3D-printed, artificially innervated soft gripper with variable joint stiffness, which has garnered 92 citations for its innovative integration of shape compliance, structural rigidity, and sensor feedback. This work addresses critical challenges in handling fragile or irregular objects, advancing the field of soft robotics. He has also pioneered the use of machine learning for joint angle prediction in cable-driven grippers, enhancing precision in manipulation tasks. Additionally, Lee’s evaluation of stacked dielectric elastomer actuators as artificial muscles for rehabilitation robots—cited 7 times—demonstrates his commitment to bridging soft robotics and biomedical engineering. With a growing citation impact, Lee’s research is shaping the future of soft, sensor-rich robotic systems for industrial and medical applications.
Research Focus
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Top Papers
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