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Autonomous Learning of Robust Visual Object Detection & Identification on a Humanoid #icdl/epirob2012
n this work we introduce a technique for a hu- manoid robot to autonomously learn the representations of objects in its visual environment. Our approach involves feature- based segmentation of the images followed by learning to identify the object using Cartesian Genetic Programming. The learned identification is able to provide robust and fast segmentation of the objects, without using features. To allow for autonomous learning an attention mechanism is coupled with the training process. We showcase our system on a humanoid robot.
n this work we introduce a technique for a hu- manoid robot to autonomously learn the representations of objects in its visual environment. Our approach involves feature- based segmentation of the images followed by learning to identify the object using Cartesian Genetic Programming. The learned identification is able to provide robust and fast segmentation of the objects, without using features. To allow for autonomous learning an attention mechanism is coupled with the training process. We showcase our system on a humanoid robot.
Autonomous Learning of Robust Visual Object Detection & Identification on a Humanoid #icdl/epirob2012
1.
Jürgen ’Juxi’ Leitner
S. Harding, P. Chandrashekhariah
M. Frank, G. Spina,
A. Förster, J. Triesch, J. Schmidhuber
idsia / usi / supsi, machine intelligence, fias
autonomous learning
of robust visual object
detection & identification
on a humanoid
#icdl/epirob 2012