Qianyi Zhang
Papers
9
Total Citations
46
H-Index
3
About
Qianyi Zhang is a researcher at the forefront of human-robot interaction and autonomous navigation, with a focus on making mobile robots safer and more perceptive in crowded, dynamic environments. Their work centers on three interconnected pillars: 3D pedestrian detection from LiDAR point clouds, human intention understanding through gaze and head pose estimation, and socially-aware motion planning. Zhang’s most impactful contributions include the AFPILD dataset (12 citations), which provides a unique acoustic and LiDAR-based benchmark for person identification and localization, and the RPEA architecture (10 citations), a residual path network with efficient attention that significantly improves 3D pedestrian detection from sparse LiDAR data. Their work on human-aware navigation, notably the paper “The Human Gaze Helps Robots Run Bravely and Efficiently in Crowds” (8 citations), addresses the critical challenge of balancing safety and efficiency by enabling robots to interpret human gaze as a cue for intention. Zhang has also advanced real-time head detection and gaze estimation (RTHG), dense cross-connections for LiDAR detection (DCCLA), and symmetry-aware 6D pose estimation for industrial bin-picking (PS6D). With a growing portfolio of over 40 citations, Zhang is shaping the future of perceptive, socially-compliant mobile robotics.
Research Focus
Key Achievements
Top Papers
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- 3The Human Gaze Helps Robots Run Bravely and Efficiently in Crowds8 citations · 2023
- 4RTHG: Towards Real- Time Head Detection And Gaze Estimation3 citations · 2022
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