Guiding Robot Model Construction with Prior Features

Erik Derner, Jiří Kubalík, Robert Babuska

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)
ISBN (Electronic)978-1-6654-1714-3
ISBN (Print)978-1-6654-1715-0
Publication statusPublished - 2021
Event2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Online at Prague, Czech Republic
Duration: 27 Sep 20211 Oct 2021


Conference2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
CountryCzech Republic
CityOnline at Prague


  • Model learning for control
  • AI-based methods
  • genetic programming
  • prior knowledge


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