Yihui Yao
Papers
2
Total Citations
13
H-Index
2
About
Yihui Yao is a robotics researcher whose work focuses on advancing dynamic modeling and safety systems for industrial and collaborative robots. His key research areas include robot dynamics, collision detection, and human-robot interaction. Yao’s major contributions lie in developing innovative, sensorless approaches to enhance robot safety and performance. In his most-cited work (2021, 9 citations), he introduced a convolution neural network-based semi-parametric dynamic model that overcomes the limitations of traditional models by eliminating complex friction hypotheses and reducing the need for extensive prior knowledge. This work has significant implications for improving robot control, collision detection, and motion planning. In another notable study (2020, 4 citations), Yao proposed a sensorless collision detection method using fuzzy logics, which avoids the cost and complexity of additional sensors while effectively handling uncertain robot dynamics. This achievement is particularly valuable for human-robot collaboration, where preventing injuries is paramount. With a growing citation record, Yao’s research is establishing him as an emerging voice in making robots safer, more efficient, and more practical for real-world industrial applications.
Research Focus
Key Achievements
Top Papers
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- 2