Zhiqing Wen
Papers
8
Total Citations
49
H-Index
5
About
Zhiqing Wen’s research lies at the intersection of robotic imitation learning, soft robotics, and computer vision, with a focus on enabling robots to learn and adapt like humans. Her major contributions include pioneering meta-learning frameworks for one-shot imitation, such as the Two-Stage Model-Agnostic Meta-Learning with Noise Mechanism (2020, 10 citations), which allows robots to learn new behaviors from a single demonstration. She also developed innovative tensegrity-based joints inspired by biological wrists and elbows, offering variable stiffness and compliance for safer human-robot interaction (2022, 10 citations; 2024, 5 citations). Her work on vision-based learning, including Target Recognition-Meta Imitation Learning (2021, 5 citations) and Replayed Task-Contrastive Meta-Learning (2022, 5 citations), advances robotic skill acquisition from visual demonstrations. Additionally, she has contributed to industrial robotics with 6D hybrid pose estimation for grasping (2022, 4 citations) and self-corrective hand-eye calibration (2022, 2 citations). With over 50 total citations, Wen’s research is shaping the future of adaptable, bionic robots capable of learning in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8