Detecting object affordances with Convolutional Neural Networks

Anh Nguyen, Dimitrios Kanoulas, Darwin G. Caldwell, Nikos G. Tsagarakis
2016 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)  
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
more » ... cy 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.
doi:10.1109/iros.2016.7759429 dblp:conf/iros/NguyenKCT16a fatcat:jvhnlje7rjbidbacihywjtcg3u