Haibo He
Papers
11
Total Citations
693
H-Index
10
About
Haibo He is a leading researcher at the intersection of adaptive dynamic programming (ADP), intelligent control, and human-robot interaction for crowd safety. His work fundamentally advances optimal control for complex, uncertain systems, including nonlinear Markov jump systems and mobile robots, where he has developed near-optimal tracking control methods using receding-horizon dual heuristic programming. A hallmark of his research is the innovative application of deep reinforcement learning to pedestrian regulation, formulating robot motion planning to optimize crowd flow and prevent disasters—a contribution cited over 80 times. He has also pioneered event-based \(H_{\infty}\) control design and multifactorial evolutionary algorithms for interval uncertainty, demonstrating a rare ability to bridge theoretical control theory with pressing real-world challenges. With top papers accumulating hundreds of citations, including his 2014 work on optimal control for Markov jump systems (152 citations), He’s impact is clear. His notable achievements include designing data-driven heuristic dynamic programming with virtual reality and developing optimal feedback control for pedestrian flow in heterogeneous corridors, cementing his reputation as a visionary in intelligent, human-centric autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 4Robot-Assisted Pedestrian Regulation Based on Deep Reinforcement Learning80 citations · 2018
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- 9Data-driven heuristic dynamic programming with virtual reality17 citations · 2015
- 10Optimal Feedback Control of Pedestrian Flow in Heterogeneous Corridors16 citations · 2020