Leitner, Juergen and Harding, Simon and Frank, Mikhail Alexander and Foerster, Alexander Uwe and Schmidhuber, Juergen (2012) Learning Spatial Object Localization from Vision on a Humanoid Robot. International Journal of Advanced Robotic Systems, 9. ISSN 1729-8806
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Abstract
We present a combined machine learning and computer vision approach for robots to localize 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 echniques, but also learn without the need for extensive calibration procedures. In addition, the approach is localizing objects robustly, when placed in the robot’s workspace at arbitrary positions, even while the robot is moving its torso, head and eyes.
| Item Type: | Scientific journal article, Newspaper article or Magazine article |
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| Uncontrolled Keywords: | genetic algorithms, genetic programming, spatial, Perception, Computer Vision, Machine Learning, Humanoid, Robotics, Object Localisation |
| Subjects: | Engineering > Electronic & electrical engineering > Robotics & cybernetics > Robotics Computer sciences > Artificial intelligence Computer sciences > Artificial intelligence > Computer vision |
| Department/unit: | Dipartimento tecnologie innovative > Istituto Dalle Molle di studi sull'intelligenza artificiale |
| Depositing User: | Juergen Leitner |
| Date Deposited: | 14 Mar 2014 07:50 |
| Last Modified: | 19 May 2016 12:10 |
| URI: | http://repository.supsi.ch/id/eprint/4262 |
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