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Simulation-aided handover prediction from video using recurrent image-to-motion networksMavsar, Matija ...Recent advances in deep neural networks have opened up new possibilities for visuomotor robot learning. In the context of human–robot or robot–robot collaboration, such networks can be trained to ... predict future poses and this information can be used to improve the dynamics of cooperative tasks. This is important, both in terms of realizing various cooperative behaviors, and for ensuring safety. In this article, we propose a recurrent neural architecture, capable of transforming variable-length input motion videos into a set of parameters describing a robot trajectory, where predictions can be made after receiving only a few frames. A simulation environment is utilized to expand the training database and to improve generalization capability of the network. The resulting architecture demonstrates good accuracy when predicting handover trajectories, with models trained on synthetic and real data showing better performance than when trained on real or simulated data only. The computed trajectories enable the execution of handover tasks with uncalibrated robots, which was verified in an experiment with two real robots.Source: IEEE transactions on neural networks and learning systems. - ISSN 2162-2388 (Vol. 35, Iss. 1, 2024, 13 str.)Type of material - e-articlePublish date - 2024Language - englishCOBISS.SI-ID - 198320131
Author
Mavsar, Matija |
Ridge, Barry |
Pahič, Rok, 1992- |
Morimoto, Jun |
Ude, Aleš
Topics
robotika |
strojno učenje
source: IEEE transactions on neural networks and learning systems. - ISSN 2162-2388 (Vol. 35, Iss. 1, 2024, 13 str.)
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Database name | Field | Year |
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Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
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Mavsar, Matija | 51232 |
Ridge, Barry | 34957 |
Pahič, Rok, 1992- | 39154 |
Morimoto, Jun | |
Ude, Aleš | 11772 |
Source: Personal bibliographies
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