Zheni Zeng
Papers
1
Total Citations
5
H-Index
1
About
Zheni Zeng is a researcher at the forefront of robotic perception and active reasoning, with a focus on enabling machines to intelligently navigate and interact with partially observable environments. Her work centers on the critical challenge of occlusion reasoning—how robots can predict the existence of objects even when they are hidden from view. In her most-cited paper, "Robotic Occlusion Reasoning for Efficient Object Existence Prediction" (2021, 5 citations), Zeng tackles this underexplored problem by developing a framework that allows robots to reason about occluded spaces and make efficient, informed predictions. This contribution bridges a key gap between active perception and practical robotic deployment, offering a foundation for more autonomous systems in cluttered, real-world settings. Her research has implications for service robotics, search-and-rescue, and autonomous navigation, where the ability to infer the unseen is paramount. Though early in her career, Zeng’s work signals a promising trajectory in advancing robot intelligence, with potential to influence how machines learn to see beyond the visible.
Research Focus
Key Achievements
Top Papers
- 1Robotic Occlusion Reasoning for Efficient Object Existence Prediction5 citations · 2021