An Empirical Investigation on Variational Autoencoder-Based Dynamic Modeling of Deformable Objects from RGB Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F24%3A00376303" target="_blank" >RIV/68407700:21730/24:00376303 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/MED61351.2024.10566173" target="_blank" >https://doi.org/10.1109/MED61351.2024.10566173</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/MED61351.2024.10566173" target="_blank" >10.1109/MED61351.2024.10566173</a>
Alternative languages
Result language
angličtina
Original language name
An Empirical Investigation on Variational Autoencoder-Based Dynamic Modeling of Deformable Objects from RGB Data
Original language description
Formulating the dynamics of continuously deformable objects and other mechanical systems analytically from first principles is an exceedingly challenging task, often impractical in real-world scenarios. What makes this challenge even harder to solve is that, usually, the object has not been observed previously, and the only information that we can get from it is a stream of RGB camera data. In this study, we explore the use of deep learning techniques to solve this nonlinear identification problem. We specifically focus on extracting dynamic models of simple deformable objects from the high-dimensional sensor input coming from an RGB camera. We investigate a two-stage approach to achieve this goal. First, we train a variational autoencoder to extract an extremely low-dimensional representation of the object configuration. Then, we learn a dynamic model that predicts the evolution of these latent space variables. The proposed architecture can accurately predict the object's state up to one second into the future.
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
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
32nd Mediterranean Conference on Control and Automation (MED)
ISBN
979-8-3503-9545-7
ISSN
2325-369X
e-ISSN
2473-3504
Number of pages
8
Pages from-to
921-928
Publisher name
IEEE Xplore
Place of publication
—
Event location
Chania, Kréta
Event date
Jun 11, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—