Learning Spatial Object Localization from Vision on a Humanoid Robot

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{Leitner:2012:IJARS,

• author = "Juergen Leitner and Simon Harding and Mikhail Frank and Alexander Forster and Jurgen Schmidhuber",
• title = "Learning Spatial Object Localization from Vision on a Humanoid Robot",
• journal = "International Journal of Advanced Robotic Systems",
• year = "2012",
• volume = "9",
• publisher = "InTech",
• keywords = "genetic algorithms, genetic programming, spatial Perception, Computer Vision, Machine Learning, Humanoid Robotics, Object Localisation",
• ISSN = "1729-8806",
• bibsource = "OAI-PMH server at www.doaj.org",
• language = "eng",
• oai = "oai:doaj-articles:31e24b4d43ea5ad01dffb4748d976920",
• URL = "http://www.intechopen.com/journals/international_journal_of_advanced_robotic_systems/learning-spatial-object-localization-from-vision-on-a-humanoid-robot",
• DOI = "doi:10.5772/54657",
• abstract = "We present a combined machine learning and computer vision approach for robots to localise objects. It allows our iCub humanoid to quickly learn to provide accurate 3D position estimates (in the centimetre range) of objects seen. Biologically inspired approaches, such as Artificial Neural Networks (ANN) and Genetic Programming (GP), are trained to provide these position estimates using the two cameras and the joint encoder readings. No camera calibration or explicit knowledge of the robot{'}s kinematic model is needed. We find that ANN and GP are not just faster and have lower complexity than traditional techniques, but also learn without the need for extensive calibration procedures. In addition, the approach is localising objects robustly, when placed in the robot's workspace at arbitrary positions, even while the robot is moving its torso, head and eyes.",

}

Genetic Programming entries for Juergen Leitner Simon Harding Mikhail Frank Alexander Forster Jurgen Schmidhuber

Citations