Object Detection Using Sim2Real Domain Randomization for Robotic Applications

Robots working in unstructured environments must be capable of sensing and interpreting their surroundings. One of the main obstacles of deep-learning-based models in the field of robotics is the lack of domain-specific labeled data for different industrial applications. In this article, we propose...

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Bibliographic Details
Published in:	IEEE transactions on robotics Vol. 39; no. 2; pp. 1225 - 1243
Main Authors:	Horvath, Daniel, Erdos, Gabor, Istenes, Zoltan, Horvath, Tomas, Foldi, Sandor
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.04.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptation models Artificial neural networks Computational modeling Computer vision for automation Datasets Deep learning deep learning in robotics and automation Industrial applications localization Object detection Object recognition Randomization Robotics Robots Service robots sim2real knowledge transfer Synthetic data Task analysis Training
ISSN:	1552-3098, 1941-0468
Online Access:	Get full text
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