Papers
17
Total Citations
509
H-Index
9
About
Qi Hao is a leading researcher in autonomous robotics and cyber-physical systems, whose work fundamentally addresses the challenge of safe, intelligent navigation in complex, dynamic environments. His core research spans multi-robot coordination, collision avoidance, and robust system design. Hao’s most influential contribution is his foundational work on robust cyber-physical systems (174 citations), which established key frameworks for integrating computation, communication, and control under uncertainty. He has pioneered the application of deep reinforcement learning to multi-robot navigation, notably developing a distributed approach that combines reciprocal velocity obstacles with learned policies (144 citations). His work on cooperative navigation in dynamic environments (57 citations) and adaptive environment modeling for collision avoidance has advanced the field’s ability to handle crowded, unpredictable scenarios. More recently, Hao introduced NeuPAN (2025), an end-to-end model-based learning system for direct point robot navigation in cluttered environments, representing a significant step toward real-time, map-free autonomy. His research also extends to practical systems, including low-cost UWB localization for indoor robots and robotic wireless energy transfer. With over 470 total citations across his top ten papers, Hao’s work is shaping the next generation of autonomous systems that can safely and efficiently operate alongside humans.
Research Focus
Key Achievements
Top Papers
- 1Robust Cyber–Physical Systems: Concept, models, and implementation174 citations · 2015
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- 6NeuPAN: Direct Point Robot Navigation With End-to-End Model-Based Learning12 citations · 2025
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