Yaojie Mao
Papers
2
Total Citations
3
H-Index
1
About
Yaojie Mao is a rising researcher in robotics, with a focus on learning from demonstration and tactile sensing for robotic manipulation. Their work addresses two critical challenges in enabling robots to perform dexterous tasks: acquiring complex skills through imitation and enhancing tactile perception for physical interaction. In their 2025 paper "Learning Manipulation from Expert Demonstrations Based on Multiple Data Associations and Physical Constraints," Mao explores how robots can learn more versatile and effective manipulation skills by mimicking human learning processes, moving beyond simple observation-action pairs. This work, already garnering 2 citations shortly after publication, highlights their contribution to making robot learning more intuitive and robust. Additionally, in "Fingertip Sensor With Magnet-Prestressed Velostat Structure for Robotic Perception and Exploration," Mao tackles the limitations of Velostat-based tactile sensors—specifically their nonlinearity and hysteresis—by introducing a novel magnet-prestressed structure. This innovation, with 1 citation to date, enhances robotic tactile perception, enabling more precise exploration and interaction with objects. Mao's research bridges the gap between skill acquisition and sensory feedback, promising to advance autonomous robotic systems in real-world applications.
Research Focus
Key Achievements
Top Papers
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- 2