Yurou Yang
Papers
1
Total Citations
2
H-Index
1
About
Yurou Yang is a rising researcher in robotics and embodied AI, with a focus on language-driven navigation and object-centric perception. Her most notable contribution is the development of LOC-ZSON (Language-driven Object-Centric Zero-Shot Object Retrieval and Navigation), a novel framework that enhances how robots understand and locate objects in complex, cluttered environments using natural language commands. By introducing an object-centric image representation and specialized loss functions for fine-tuning vision-language models (VLMs), Yang’s work enables zero-shot object navigation—allowing robots to find unfamiliar objects without prior training. This approach addresses a critical challenge in embodied AI: bridging the gap between human language and robotic spatial reasoning. While her 2024 paper has garnered early citations, reflecting the timeliness and potential of her research, Yang’s work is positioned at the intersection of computer vision, natural language processing, and robotics. Her contributions are particularly relevant for applications in service robotics, autonomous exploration, and human-robot interaction, where intuitive communication and robust perception are essential. As a young researcher, Yang is already shaping the future of intelligent, language-guided robotic systems.
Research Focus
Key Achievements
Top Papers
- 1