Xingqun Zhou
Papers
4
Total Citations
35
H-Index
3
About
Xingqun Zhou is a leading researcher at the intersection of human-robot interaction, underwater robotics, and intelligent control systems. Their work fundamentally advances how robots understand and collaborate with humans, particularly through programming by demonstration (PbD) and implicit human-computer interaction. Zhou’s most cited paper (22 citations) introduces a novel PbD framework that fuses human muscular task planning with robot manipulation, enabling robots to replicate delicate, high-fidelity finger movements from human demonstrations—a breakthrough for industrial automation. They further pioneer compliant robotic grasping using surface electromyography and inertial sensors, allowing cooperative robots to interpret subconscious human gestures for safer, more intuitive collaboration. In underwater robotics, Zhou’s localization method for AUV swarms (8 citations) employs enhanced visual markers to achieve precise positioning in challenging deep-sea environments, supporting large-scale exploration and mapping. Their application of deep learning for gesture recognition in intelligent wheelchair control demonstrates a commitment to assistive technology. With a growing citation impact and a portfolio spanning industrial, marine, and rehabilitation robotics, Zhou’s work is shaping the future of seamless human-robot teamwork.
Research Focus
Key Achievements
Top Papers
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