Detecting object affordances with Convolutional Neural Networks
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.
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)