Zhanteng Xie
Papers
5
Total Citations
204
H-Index
4
About
Zhanteng Xie is a robotics researcher whose work lies at the intersection of autonomous navigation, deep reinforcement learning, and multi-target tracking. Xie’s most impactful contribution is the development of DRL-VO, a novel learning-based control policy that enables mobile robots to navigate safely through crowded, dynamic environments filled with both static obstacles and dense pedestrian crowds. This work, which has garnered over 120 citations, demonstrates strong generalizability to new, unseen spaces—a critical challenge in real-world robotics. Xie also made key advances in multi-class target tracking through the semantic PHD filter, bridging theory and practical deployment. Further showcasing expertise in constrained navigation, Xie contributed to the Benchmark Autonomous Robot Navigation (BARN) Challenge at ICRA 2022, which rigorously evaluated state-of-the-art systems in highly confined spaces. With a focus on safety and robustness, Xie’s research has laid important groundwork for deploying autonomous robots in human-centric environments, from warehouses to public spaces.
Research Focus
Key Achievements
Top Papers
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- 4Towards Safe Navigation Through Crowded Dynamic Environments23 citations · 2021
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