Guiding Robot Model Construction with Prior Features

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00353560" target="_blank" >RIV/68407700:21230/21:00353560 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/21:00353560

Výsledek na webu

<a href="https://doi.org/10.1109/IROS51168.2021.9635831" target="_blank" >https://doi.org/10.1109/IROS51168.2021.9635831</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IROS51168.2021.9635831" target="_blank" >10.1109/IROS51168.2021.9635831</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Guiding Robot Model Construction with Prior Features

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Guiding Robot Model Construction with Prior Features

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotika pro Průmysl 4.0</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

ISBN

978-1-6654-1714-3

ISSN

2153-0858

e-ISSN

2153-0866

Počet stran výsledku

7

Strana od-do

7112-7118

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Praha

Datum konání akce

27. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000755125505101