Papers
2
Total Citations
21
H-Index
2
About
Yue Mao is a rising figure in the field of robotic manipulation and sports robotics, with a primary focus on table tennis robot systems. His research bridges the gap between classical control theory and modern reinforcement learning to address the unique challenges of high-speed, dynamic ball interception. Mao’s major contribution is the development of a novel model-based framework that integrates trajectory prediction with precise hitting velocity control, moving beyond standard position control to meet the real-world demands of ball speed and spin. This work, published in 2021 and garnering 19 citations, has established a foundational approach for more responsive and competitive table tennis robots. Building on this, Mao has advanced the field by applying end-to-end curriculum reinforcement learning to enable robots to catch spinning balls in simulation—a notoriously difficult problem requiring adaptive policy learning. His work demonstrates a clear trajectory from theoretical modeling to practical, learning-based solutions. As a researcher, Mao is notable for his ability to combine mechanical design with intelligent control, positioning him at the forefront of creating robots that can match human dexterity in fast-paced interactive sports.
Research Focus
Key Achievements
Top Papers
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- 2