Papers
6
Total Citations
170
H-Index
4
About
Congcong Liu is a leading researcher in the intersection of autonomous navigation, human-robot interaction, and graph-based machine learning. Their work focuses on enabling mobile robots to navigate safely and efficiently through crowded, dynamic environments—a critical challenge for real-world deployment in spaces like shopping malls, airports, and city streets. Liu’s major contributions include pioneering the use of Graph Convolutional Networks (GCNs) with attention mechanisms that learn from human gaze patterns, as demonstrated in their highly cited 2020 paper (139 citations), which significantly improved robot navigation performance in dense crowds where traditional deep reinforcement learning fails. They further advanced the field by developing hierarchical graph architectures for pedestrian trajectory prediction, notably HGCN-GJS and CoMoGCN, which model social interactions and group coherence to forecast human motion more accurately. Liu’s work has garnered over 170 citations, underscoring its impact on autonomous driving and mobile robotics. By integrating cognitive cues from human behavior with graph-based representations, Liu has laid a foundation for more intuitive and socially aware robot navigation, making them a key figure in the push toward truly autonomous systems that can coexist with humans.
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