Papers
2
Total Citations
17
H-Index
2
About
Ye Lu is a researcher whose work sits at the intersection of robotics, artificial intelligence, and human-robot interaction, with a particular focus on enabling robots to operate safely and intelligently alongside people in real-world environments. Lu's most notable contribution, "Human-Aware Robot Navigation via Reinforcement Learning with Hindsight Experience Replay and Curriculum Learning" (2021), addresses one of the field's most pressing challenges: navigating dense crowds efficiently and safely. By leveraging advanced reinforcement learning techniques — specifically combining hindsight experience replay with curriculum learning — Lu's approach pushes the boundaries of sequential decision-making in dynamic social spaces, earning 10 citations since its publication. Complementing this technical work, Lu's earlier research on the "Stepped Warm-Up" interaction framework (2019, 7 citations) demonstrates a thoughtful understanding of how robots should progressively engage with humans in public settings, recognizing that effective human-robot interaction requires careful social calibration beyond pure algorithmic performance. Together, these contributions reflect a researcher who bridges rigorous machine learning methodology with human-centered design principles — making Lu's work particularly valuable for students and practitioners working on the next generation of socially intelligent service robots.
Research Focus
Key Achievements
Top Papers
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