Papers
3
Total Citations
35
H-Index
2
About
Yue Lu’s research lies at the intersection of embodied artificial intelligence, reinforcement learning (RL), and human-robot interaction, with a particular focus on enabling intelligent agents to navigate and operate in complex, dynamic environments. A key contribution is the development of transformer-based memory architectures for interactive visual navigation, allowing robots to reason about and manipulate cluttered, non-stationary scenes—a significant step beyond traditional RL methods that assume static obstacles. This work, published in 2023, has already garnered 22 citations for its practical implications in autonomous robotics. Lu also addresses the fundamental challenge of causal confusion in RL, proposing methods to identify causally correct inputs and apply targeted interventions, thereby improving generalization and robustness in tasks like robot navigation. Beyond technical algorithms, Lu explores the conceptual and applied dimensions of AI-driven agents in tourism and hospitality, using qualitative methods to characterize “AI dogs” and their potential roles alongside real animals and humanoid robots. This interdisciplinary work, with 11 citations, demonstrates a rare ability to bridge engineering rigor with social science inquiry. With a growing citation record and a portfolio that spans algorithmic innovation to real-world deployment, Yue Lu is shaping how machines learn, move, and interact with the physical world.
Research Focus
Key Achievements
Top Papers
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