Guiding Robot Model Construction with Prior Features
The result's identifiers
Result code in 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>
Alternative codes found
RIV/68407700:21730/21:00353560
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Guiding Robot Model Construction with Prior Features
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotics 4 Industry 4.0</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
ISBN
978-1-6654-1714-3
ISSN
2153-0858
e-ISSN
2153-0866
Number of pages
7
Pages from-to
7112-7118
Publisher name
IEEE
Place of publication
Piscataway
Event location
Praha
Event date
Sep 27, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000755125505101