Haitong Wang
Papers
4
Total Citations
43
H-Index
2
About
Haitong Wang is an emerging researcher at the forefront of autonomous mobile robotics, with a focus on robot navigation, target-driven search, and human-robot interaction in complex, real-world environments. His work addresses some of the most pressing challenges in robotics: enabling robots to navigate unknown, dynamic, and cluttered spaces — such as disaster scenes and healthcare facilities — without relying on predefined maps or complete environmental knowledge. Wang's most influential contribution, NavFormer (2024), introduced a transformer-based architecture for target-driven robot navigation, garnering 33 citations and demonstrating the power of modern deep learning frameworks for robust autonomous movement. His research further pushes boundaries by exploring how vision-language models can interpret hand-drawn maps for robot navigation, bridging informal human communication with machine understanding. His MLLM-Search work showcases a zero-shot approach using multimodal large language models to locate people in dynamic environments, a capability with significant implications for healthcare robotics and search-and-rescue applications. Collectively, Wang's research reflects a compelling vision: making robots more adaptable, intelligent, and genuinely useful in unpredictable human-centered environments, establishing him as a promising voice in the next generation of robotics researchers.
Research Focus
Key Achievements
Top Papers
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