Haolin Fei
Papers
5
Total Citations
27
H-Index
3
About
Haolin Fei is a rising researcher at the intersection of robotics, human-robot interaction, and intelligent control systems. His work centers on making robots more intuitive and autonomous partners for humans, with a particular focus on bimanual teleoperation—where a single operator controls two robotic arms simultaneously. Fei’s major contributions include developing methods to assist teleoperation by learning from natural human limb coordination, using interval type-2 polynomial fuzzy inference to reduce cognitive load. He has also advanced visual servoing, comparing RGB-based algorithms and integrating robust reinforcement learning with convolutional features to handle real-world interference like lighting changes. His most cited work, “Learning to Assist Bimanual Teleoperation” (14 citations), demonstrates his impact in this niche. More recently, Fei has explored hand gesture decoding with neural networks and large language model-driven interfaces for natural language control, pushing toward seamless, task-independent teleoperation. His research, though early in its trajectory, is already shaping how robots can assist in hazardous environments and collaborative tasks, earning him recognition as an innovator in robotic assistance and autonomy.
Research Focus
Key Achievements
Top Papers
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- 2Boosting visual servoing performance through RGB-based methods5 citations · 2023
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