Chung-Che Yu
Papers
1
Total Citations
6
H-Index
1
About
Chung-Che Yu is a leading researcher in robotics and autonomous navigation, with a focus on developing intelligent systems that operate safely in dynamic, human-populated environments. His work addresses two critical challenges: collision-free navigation and the "freezing robot problem," where robots become paralyzed in crowded spaces. Yu's most-cited paper, "Collision- and Freezing-Free Navigation in Dynamic Environments Using Learning to Search" (2012, 6 citations), pioneered the use of learning from demonstration (LfD) to replace tedious rule-based approaches. By enabling robots to learn navigation behaviors from human examples, his research significantly reduces the need for manual parameter tuning while ensuring robust performance in complex scenarios. This work has influenced subsequent studies in social robotics and human-robot interaction, demonstrating how machine learning can overcome the limitations of traditional planning algorithms. Yu's contributions are particularly notable for bridging the gap between theoretical search methods and practical deployment, offering a scalable solution for autonomous systems in crowded spaces like hospitals, warehouses, and public areas. His research continues to inspire new approaches in adaptive, learning-driven navigation.
Research Focus
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Top Papers
- 1