MyGym: Modular Toolkit for Visuomotor Robotic Tasks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F21%3A00354192" target="_blank" >RIV/68407700:21730/21:00354192 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICTAI52525.2021.00046" target="_blank" >https://doi.org/10.1109/ICTAI52525.2021.00046</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICTAI52525.2021.00046" target="_blank" >10.1109/ICTAI52525.2021.00046</a>

Result language
angličtina
Original language name
MyGym: Modular Toolkit for Visuomotor Robotic Tasks
Original language description
We introduce myGym, a toolkit suitable for fast prototyping of neural networks in the area of robotic manipulation and navigation. Our toolbox is fully modular, enabling users to train their algorithms on different robots, environments, and tasks. We also include pretrained neural network modules for the real-time vision that allows training visuomotor tasks with sim2real transfer. The visual modules can be easily retrained using the dataset generation pipeline with domain augmentation and randomization. Moreover, myGym provides automatic evaluation methods and baselines that help the user to directly compare their trained model with the state-of-the-art algorithms. We additionally present a novel metric, called learnability, to compare the general learning capability of algorithms in different settings, where the complexity of the environment, robot, and the task is systematically manipulated. The learnability score tracks differences between the performance of algorithms in increasingly challenging setup conditions, and thus allows the user to compare different models in a more systematic fashion. The code is accessible at https://github.com/incognite-lab/myGym
Czech name
—
Czech description
—

Project
<a href="/en/project/EF16_019%2F0000766" target="_blank" >EF16_019/0000766: Engineering applications of microworld physics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)
ISBN
978-1-6654-0898-1
ISSN
1082-3409
e-ISSN
2375-0197
Number of pages
5
Pages from-to
279-283
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Washington
Event date
Nov 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000747482300038

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