Zoe Dougleri
Papers
1
Total Citations
2
H-Index
1
About
Zoe Dougleri is a robotics researcher whose work centers on perception, occlusion detection, and safe human-robot interaction. Her most cited paper, "A Lightweight Method for Detecting Dynamic Target Occlusions by the Robot Body" (2023), introduces an efficient, real-time approach to identifying when a robot’s own structure blocks its view of moving objects—a critical challenge for autonomous navigation and manipulation in cluttered environments. Though early in her career, this contribution has already garnered 2 citations, signaling its relevance to the growing field of embodied AI. Dougleri’s method prioritizes computational lightness, making it suitable for resource-constrained robotic platforms, and directly addresses safety concerns in collaborative settings. Her work bridges computer vision and robotics, offering practical solutions for dynamic occlusion handling that could enhance robot autonomy in warehouses, homes, and healthcare. As she continues to publish, Dougleri is establishing herself as a thoughtful engineer focused on real-world robustness, with potential to influence how robots perceive and act in unpredictable spaces.
Research Focus
Key Achievements
Top Papers
- 1