One Step Deep Learning Approach to Grasp Detection in Robotics

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F21%3A39918034" target="_blank" >RIV/00216275:25530/21:39918034 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-90321-3_2" target="_blank" >http://dx.doi.org/10.1007/978-3-030-90321-3_2</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-90321-3_2" target="_blank" >10.1007/978-3-030-90321-3_2</a>

Result language
angličtina
Original language name
One Step Deep Learning Approach to Grasp Detection in Robotics
Original language description
Grasp point detection is a necessary ability to handle for industrial robots. In recent years, various deep learning-based techniques for robotic grasping have been introduced. To follow this trend, we introduce a convolutional neural network-based approach for model-free one step method for grasp point detection. This method provides all feasible grasp points suitable for parallel grippers, based on a single RGB image of the scene. A case study, which shows the outstanding accuracy of the presented approach as well as its acceptable response time, is presented at the end of this contribution.
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Data Science and Intelligent Systems : proceedings of 5th Computational Methods in Systems and Software 2021, Vol. 2
ISBN
978-3-030-90320-6
ISSN
2367-3370
e-ISSN
2367-3389
Number of pages
10
Pages from-to
8-17
Publisher name
Springer Science and Business Media
Place of publication
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Event location
ONLINE
Event date
Oct 1, 2021
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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