Jonathan Styrud
Papers
10
Total Citations
406
H-Index
7
About
Jonathan Styrud is a robotics and AI researcher whose work sits at the intersection of autonomous robot programming, adaptive control architectures, and intelligent decision-making. He is best known for his extensive contributions to the theory and application of Behavior Trees (BTs) in robotics, co-authoring a landmark survey on the topic that has accumulated over 250 citations — making it one of the most referenced works in the field. This survey, alongside an earlier 2020 version with 31 citations, has become essential reading for researchers transitioning from classical Finite State Machines to more modular, scalable AI architectures. Beyond surveying the landscape, Styrud has actively pushed the boundaries of how robots learn and adapt. His research integrates planning, learning from demonstration, genetic programming, Bayesian optimization, and most recently, large language models to automatically generate and expand BTs for manipulation tasks in unpredictable environments. This body of work directly addresses the real-world challenge of programming industrial and collaborative robots quickly and intuitively. His earlier work on friction modeling in strain wave gears (27 citations) further demonstrates his breadth across both mechanical and computational domains. Collectively, Styrud's research offers a compelling vision of robots that are not only reactive and robust, but genuinely easier for humans to configure and trust.
Research Focus
Key Achievements
Top Papers
- 1A survey of Behavior Trees in robotics and AI250 citations · 2022
- 2Combining Planning and Learning of Behavior Trees for Robotic Assembly39 citations · 2022
- 3A Survey of Behavior Trees in Robotics and AI31 citations · 2020
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- 8Automatic Behavior Tree Expansion with LLMs for Robotic Manipulation6 citations · 2025
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