Jinge Qie
Papers
1
Total Citations
4
H-Index
1
About
Jinge Qie is a rising researcher at the intersection of computer vision, robotics, and human-robot interaction, with a primary focus on deformable object manipulation. Her most-cited work, "Cross-Domain Representation Learning for Clothes Unfolding in Robot-Assisted Dressing" (2023), tackles a fundamental challenge in assistive robotics: enabling robots to autonomously handle non-rigid fabrics. By developing a cross-domain representation learning framework, Qie bridges the gap between simulated training and real-world cloth manipulation, allowing robots to generalize unfolding strategies across diverse garment types and configurations. This contribution is critical for advancing robot-assisted dressing systems, which aim to support individuals with limited mobility. Though early in her career, her work has already garnered attention (4 citations), reflecting its novelty and practical relevance. Qie’s research not only pushes the boundaries of robotic perception and control but also addresses a pressing societal need for accessible, dignified care. Her approach—combining deep learning with physical reasoning—positions her as a promising voice in the growing field of assistive robotics, where her future work is likely to further bridge the gap between laboratory innovation and real-world deployment.
Research Focus
Key Achievements
Top Papers
- 1