Learning Behavior Trees with Genetic Programming in Unpredictable Environments

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@InProceedings{Iovino:2021:ICRA,

• author = "Matteo Iovino and Jonathan Styrud and Pietro Falco and Christian Smith",
• title = "Learning Behavior Trees with Genetic Programming in Unpredictable Environments",
• booktitle = "2021 IEEE International Conference on Robotics and Automation (ICRA)",
• year = "2021",
• pages = "4591--4597",
• abstract = "Modern industrial applications require robots to operate in unpredictable environments, and programs to be created with a minimal effort, to accommodate frequent changes to the task. Here, we show that genetic programming can be effectively used to learn the structure of a behavior tree (BT) to solve a robotic task in an unpredictable environment. We propose to use a simple simulator for learning, and demonstrate that the learned BTs can solve the same task in a realistic simulator, converging without the need for task specific heuristics, making our method appealing for real robotic applications.",
• keywords = "genetic algorithms, genetic programming, Automation, Service robots, Conferences, Task analysis, Behavior Trees, Mobile Manipulation",
• DOI = "10.1109/ICRA48506.2021.9562088",
• ISSN = "2577-087X",
• month = may,
• notes = "Also known as \cite{9562088}",

}

Genetic Programming entries for Matteo Iovino Jonathan Styrud Pietro Falco Christian Smith

Citations