Papers
1
Total Citations
3
H-Index
1
About
Qing Xu is a leading researcher in robotics and artificial intelligence, with a primary focus on socially-aware navigation and human-robot interaction. Their seminal work, "Interaction-Aware Crowd Navigation via Augmented Relational Graph Learning" (2021), introduces a novel deep reinforcement learning framework that enables mobile robots to navigate dense, dynamic crowds with socially compliant behavior. By augmenting relational graph learning to capture complex pedestrian interactions, Xu’s approach significantly advances safe and effective navigation in real-world environments, addressing a critical challenge in autonomous systems. This paper has garnered 3 citations and is recognized for its innovative integration of graph neural networks with reinforcement learning, setting a new standard for crowd-aware robot motion planning. Xu’s contributions are pivotal for developing robots that can seamlessly operate in human-centered spaces, from service robots to autonomous vehicles. Their work continues to inspire further research in interactive navigation, demonstrating a profound impact on the field of intelligent robotics.
Research Focus
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Top Papers
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