Conference item
DEFO-NET: Learning body deformation using generative adversarial networks
- Abstract:
-
Modelling the physical properties of everyday objects is a fundamental prerequisite for autonomous robots. We present a novel generative adversarial network (DEFO-NET), able to predict body deformations under external forces from a single RGB-D image. The network is based on an invertible conditional Generative Adversarial Network (IcGAN) and is trained on a collection of different objects of interest generated by a physical finite element model simulator. Defo-netinherits the generalisation ...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Bibliographic Details
- Publisher:
- IEEE Publisher's website
- Host title:
- 2018 IEEE International Conference on Robotics and Automation (ICRA)
- Journal:
- IEEE International Conference on Robotics and Automation (ICRA 2018) Journal website
- Pages:
- 2440-2447
- Publication date:
- 2018-09-13
- Acceptance date:
- 2018-01-08
- DOI:
- ISSN:
-
2577-087X
- ISBN:
- 9781538630815
Item Description
- Pubs id:
-
pubs:827797
- UUID:
-
uuid:36b98aa4-1711-47f9-91de-0e41e9c5baeb
- Local pid:
- pubs:827797
- Source identifiers:
-
827797
- Deposit date:
- 2018-03-05
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2018
- Notes:
- Copyright © 2018 IEEE. This is the accepted manuscript version of the article. The final version is available online from IEEE at: https://doi.org/10.1109/ICRA.2018.8462832
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