Papers
13
Total Citations
207
H-Index
8
About
Wu Yan is a leading researcher in robotics, with a focus on enabling robots to learn complex manipulation skills from human demonstrations. Her work bridges the gap between human intuition and robotic precision, tackling challenges in kinematic control, object pose estimation, and skill acquisition. She has made significant contributions to dynamic neural network-based control for redundant manipulators, addressing model uncertainties to improve robotic dexterity. Her highly cited paper on "Dynamic neural networks based kinematic control for redundant manipulators with model uncertainties" (62 citations) underscores her impact in this area. Yan has also advanced robot learning from demonstration, developing frameworks for robots to acquire skills from complex, long-horizon tasks, as seen in her 2021 work (30 citations). Her research on fast object pose estimation using adaptive thresholds for bin-picking (30 citations) has practical implications for manufacturing and logistics. Additionally, she has explored intuitive human-robot interaction, such as controlling robots via Leap Motion gestures (28 citations). With over 200 total citations across her publications, Yan's work is shaping the future of autonomous robotic systems, making them more adaptable, efficient, and collaborative in real-world environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Framework of Robot Skill Learning From Complex and Long-Horizon Tasks30 citations · 2021
- 3Fast Object Pose Estimation Using Adaptive Threshold for Bin-Picking30 citations · 2020
- 4Controlling a robot using leap motion28 citations · 2017
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- 86D Pose Estimation with Correlation Fusion8 citations · 2019
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