Improving robot vision models for object detection through interaction

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Leitner:2014:IJCNN,

• author = "J. Leitner and A. Foerster and J. Schmidhuber",
• booktitle = "International Joint Conference on Neural Networks (IJCNN 2014)",
• title = "Improving robot vision models for object detection through interaction",
• year = "2014",
• month = jul,
• pages = "3355--3362",
• abstract = "We propose a method for learning specific object representations that can be applied (and reused) in visual detection and identification tasks. A machine learning technique called Cartesian Genetic Programming (CGP) is used to create these models based on a series of images. Our research investigates how manipulation actions might allow for the development of better visual models and therefore better robot vision. This paper describes how visual object representations can be learnt and improved by performing object manipulation actions, such as, poke, push and pick-up with a humanoid robot. The improvement can be measured and allows for the robot to select and perform the `right' action, i.e. the action with the best possible improvement of the detector.",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming",
• DOI = "doi:10.1109/IJCNN.2014.6889556",
• notes = "Also known as \cite{6889556}",

}

Genetic Programming entries for Juergen Leitner A Foerster Jurgen Schmidhuber

Citations