UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Detecting Object Affordances with Convolutional Neural Networks

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. Green open access

[img]
Preview
Text
P9__Nguyen_Kanoulas_Caldwell_Tsagarakis__2016__Detecting_Object_Affordances_with_Convolutional_Neural_Networks.pdf - Accepted version

Download (5MB) | Preview

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
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 > 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
Downloads since deposit
135Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item