Guiding Robot Model Construction with Prior Features

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Derner:2021:IROS,

• author = "Erik Derner and Jiri Kubalik and Robert Babuska",
• title = "Guiding Robot Model Construction with Prior Features",
• booktitle = "2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",
• year = "2021",
• pages = "7112--7118",
• abstract = "Virtually all robot control methods benefit from the availability of an accurate mathematical model of the robot. However, obtaining a sufficient amount of informative data for constructing dynamic models can be difficult, especially when the models are to be learned during robot deployment. Under such circumstances, standard data-driven model learning techniques often yield models that do not comply with the physics of the robot. We extend a symbolic regression algorithm based on Single Node Genetic Programming by including the prior model information into the model construction process. In this way, symbolic regression automatically builds models that compensate for theoretical or empirical model deficiencies. We experimentally demonstrate the approach on two real-world systems: the TurtleBot 2 mobile robot and the Parrot Bebop 2 drone. The results show that the proposed model-learning algorithm produces realistic models that fit well the training data even when using small training sets. Passing the prior model information to the algorithm significantly improves the model accuracy while speeding up the search.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/IROS51168.2021.9635831",
• ISSN = "2153-0866",
• month = sep,
• notes = "Also known as \cite{9635831}",

}

Genetic Programming entries for Erik Derner Jiri Kubalik Robert Babuska

Citations