Peter Hilgers
Papers
1
Total Citations
39
H-Index
1
About
Peter Hilgers is a pioneer in the application of model-free reinforcement learning to autonomous aerial robotics, with his most-cited work focusing on the control of autonomous blimps. His key research areas span machine learning, robotics, and intelligent control systems. Hilgers’s major contribution lies in demonstrating that reinforcement learning can be used to learn effective control policies for continuous state and action spaces without requiring complex, hand-tuned models—a significant advancement for real-world robotic applications. His seminal 2007 paper, "Autonomous blimp control using model-free reinforcement learning in a continuous state and action space," has garnered 39 citations, serving as a foundational reference for researchers exploring online learning in aerial vehicles. This work showcased how a blimp could autonomously learn height control through trial and error, highlighting the potential of model-free approaches in dynamic environments. Hilgers’s research continues to influence the fields of autonomous systems and adaptive control, inspiring new generations of roboticists to leverage reinforcement learning for challenging, real-time control problems.
Research Focus
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Top Papers
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