Yuwei Liang
Papers
3
Total Citations
32
H-Index
3
About
Yuwei Liang is a robotics researcher whose work focuses on bridging the gap between human dexterity and robotic manipulation. Her primary research areas include robotic motion planning, human-robot interaction, and deep learning for grasp planning. Liang’s most notable contribution is her pioneering work on enabling service robots to perform complex dual-arm sign language motions. In her highly cited 2021 paper (23 citations), she developed a Dynamic Movement Primitive-based framework for motion retargeting, solving the intricate challenge of transferring coordinated arm and hand gestures from human demonstrators to robots—a critical step toward more inclusive human-robot communication. Additionally, Liang proposed a cascaded deep learning framework for real-time and robust grasp planning (2019, 6 citations), addressing the longstanding trade-off between computational efficiency and grasping reliability. Her work stands out for its practical focus: rather than pursuing theoretical advances alone, Liang’s algorithms are designed for real-world deployment in service robotics. By tackling both the expressive demands of sign language and the functional demands of object manipulation, she is helping to create robots that are not only more capable but also more socially aware.
Research Focus
Key Achievements
Top Papers
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