Xiao Bi
Papers
2
Total Citations
16
H-Index
2
About
Xiao Bi is a researcher focused on advancing active perception and autonomous navigation, with a particular emphasis on **Active Object Tracking (AOT)**. Their major contribution lies in developing generalizable frameworks that enable autonomous systems—such as mobile robots and self-driving vehicles—to maintain consistent visual contact with moving objects. Bi’s most cited work, **“RSPT: Reconstruct Surroundings and Predict Trajectories for Generalizable Active Object Tracking”** (2023, 14 citations), introduces a novel approach that integrates environmental reconstruction with trajectory prediction. This method allows a tracker to anticipate an object’s future path while adapting to complex, dynamic surroundings, significantly improving robustness across diverse scenarios. By addressing the long-standing challenge of generalization in AOT, Bi’s research bridges the gap between controlled lab settings and real-world deployment. Their work is particularly notable for its practical impact on autonomous driving and mobile robotics, where reliable object tracking is critical for safety and efficiency. With a growing citation footprint, Xiao Bi is establishing themselves as a key contributor to the fields of active vision and intelligent motion control.
Research Focus
Key Achievements
Top Papers
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- 2