Improving robot vision models for object detection through interaction

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  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