Yuki Hyodo
Papers
1
Total Citations
4
H-Index
1
About
Yuki Hyodo is a researcher focused on advancing mobile robot navigation in crowded, human-populated environments—a critical challenge for deploying service robots in daily life. Their most-cited work, "Crowd-Aware Robot Navigation with Switching Between Learning-Based and Rule-Based Methods Using Normalizing Flows" (2024), introduces a novel hybrid framework that dynamically switches between deep reinforcement learning and rule-based control to improve safety and efficiency around pedestrians. This approach addresses a key limitation of purely learning-based methods, which can struggle with unpredictable human behavior. With 4 citations in a short time, this paper signals growing interest in their practical, robust solutions. Hyodo’s contributions lie at the intersection of robotics, artificial intelligence, and human-robot interaction, offering scalable navigation strategies that balance adaptability with reliability. Their work is particularly valuable for students and researchers seeking to bridge the gap between simulation-trained policies and real-world deployment, making autonomous robots safer and more trustworthy in crowded spaces.
Research Focus
Key Achievements
Top Papers
- 1