Jingjing Zheng
Papers
2
Total Citations
15
H-Index
2
About
Jingjing Zheng’s research bridges the critical gap between robotic perception and mechanical adaptability, with a focus on enabling machines to navigate complex, unstructured environments. Her key contributions lie in two complementary areas: monocular depth estimation for robotic vision and novel deformable locomotion mechanisms. In her highly cited 2022 work, *Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation*, Zheng tackled a fundamental challenge in deep learning—enabling models trained on synthetic data to generalize to real-world scenes. This approach, which has garnered 11 citations, is vital for low-cost robotic tasks like obstacle detection and mapping, where ground-truth depth data is scarce. More recently, her 2024 study, *Design and research of deformable wheel-legged robot based on origami mechanisms*, introduces a groundbreaking robot that uses origami-inspired structures to transition between wheeled and legged locomotion. With 4 citations already, this work directly addresses the limited adaptability of traditional wheeled robots, offering a lightweight, transformable solution for search-and-rescue or planetary exploration. Zheng’s dual focus on perception and hardware innovation marks her as a rising figure in robotics, where her work promises to make autonomous systems both smarter and more physically versatile.
Research Focus
Key Achievements
Top Papers
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- 2