A Framework for Learning Behavior Trees in Collaborative Robotic Applications

Created by W.Langdon from gp-bibliography.bib Revision:1.8098

@InProceedings{Iovino:2023:CASE,

• author = "Matteo Iovino and Jonathan Styrud and Pietro Falco and Christian Smith",
• booktitle = "2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)",
• title = "A Framework for Learning Behavior Trees in Collaborative Robotic Applications",
• year = "2023",
• abstract = "In modern industrial collaborative robotic applications, it is desirable to create robot programs automatically, intuitively, and time-efficiently. Moreover, robots need to be controlled by reactive policies to face the unpredictability of the environment they operate in. In this paper we propose a framework that combines a method that learns Behaviour Trees (BTs) from demonstration with a method that evolves them with Genetic Programming (GP) for collaborative robotic applications. The main contribution of this paper is to show that by combining the two learning methods we obtain a method that allows non-expert users to semi-automatically, time-efficiently, and interactively generate BTs. We validate the framework with a series of manipulation experiments. The BT is fully learnt in simulation and then transferred to a real collaborative robot.",
• keywords = "genetic algorithms, genetic programming, Learning systems, Computer aided software engineering, Automation, Service robots, Collaboration, Behavioural sciences, Behaviour Trees, Learning from Demonstration, Collaborative Robotics",
• DOI = "doi:10.1109/CASE56687.2023.10260363",
• ISSN = "2161-8089",
• month = aug,
• notes = "Also known as \cite{10260363}",

}

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

Citations