Papers
5
Total Citations
26
H-Index
3
About
Yufei Zhu is a leading researcher in human-aware robot navigation and social robotics, with a focus on enabling autonomous systems to safely and efficiently operate in environments shared with humans. Their work centers on modeling, predicting, and understanding human motion, particularly through the development of large-scale datasets and advanced machine learning techniques. Zhu's most notable contribution is the creation of **THÖR-MAGNI**, a large-scale indoor motion capture dataset of human and robot interaction, which has already garnered **14 citations** since its 2024 publication and serves as a critical resource for studying social navigation, human motion prediction, and goal-oriented human-robot interaction. They have also pioneered **fast online learning of CLiFF-Maps** for changing environments, enabling robots to adapt to dynamic human behaviors in real time. Further impact includes robust obstacle avoidance through multi-sensor data fusion, trajectory prediction for heterogeneous agents in imbalanced datasets, and long-term human motion prediction using spatio-temporal maps. Zhu's research bridges the gap between perception and action, providing foundational tools and insights that advance the fields of human-robot interaction, autonomous navigation, and intelligent transportation systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Fast Online Learning of CLiFF-Maps in Changing Environments3 citations · 2025
- 4
- 5Long-Term Human Motion Prediction Using Spatio-Temporal Maps of Dynamics1 citations · 2025