Papers
9
Total Citations
74
H-Index
5
About
Qing Tao is a versatile robotics and human-machine interaction researcher whose work spans two deeply interconnected domains: autonomous robot perception and neural-driven human-robot interfaces. In the field of Semantic SLAM, Tao has made notable contributions to object association and dynamic scene understanding, developing Bayesian nonparametric frameworks, probabilistic detection methods, and spatiotemporal consistency constraints to improve the robustness and accuracy of mobile robot localization and mapping — work that has collectively attracted over 35 citations. Equally significant is Tao's research in biosignal-driven control systems, where surface electromyography (sEMG) is leveraged to decode continuous joint motion across both upper and lower limbs, enabling more natural prosthetic control, exoskeleton operation, and rehabilitation assessment. His 2022 paper on simultaneous hand joint angle estimation has already garnered 14 citations, reflecting strong community interest. Tao has further explored EEG-based control paradigms and robot workspace optimization, demonstrating a broad engineering vision. With publications spanning 2019 to 2025 and a steadily growing citation record, Tao represents an emerging voice in intelligent robotics and assistive technology research.
Research Focus
Key Achievements
Top Papers
- 1Bayesian Nonparametric Object Association for Semantic SLAM15 citations · 2021
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