Anh, N;
Kanoulas, D;
Caldwell, DG;
Tsagarakis, NG;
(2016)
Detecting Object Affordances with Convolutional Neural Networks.
In:
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
(pp. pp. 2765-2770).
IEEE: Daejeon, South Korea.
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Abstract
We present a novel and real-time method to detect object affordances from RGB-D images. Our method trains a deep Convolutional Neural Network (CNN) to learn deep features from the input data in an end-to-end manner. The CNN has an encoder-decoder architecture in order to obtain smooth label predictions. The input data are represented as multiple modalities to let the network learn the features more effectively. Our method sets a new benchmark on detecting object affordances, improving the accuracy by 20% in comparison with the state-of-the-art methods that use hand-designed geometric features. Furthermore, we apply our detection method on a full-size humanoid robot (WALK-MAN) to demonstrate that the robot is able to perform grasps after efficiently detecting the object affordances.
Type: | Proceedings paper |
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Title: | Detecting Object Affordances with Convolutional Neural Networks |
Event: | IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
Location: | Daejeon, SOUTH KOREA |
Dates: | 09 October 2016 - 14 October 2016 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/IROS.2016.7759429 |
Publisher version: | https://doi.org/10.1109/IROS.2016.7759429 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10083222 |
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