Autonomous learning of robust visual object detection and identification on a humanoid

Jürgen Leitner, Pramod Chandrashekhariah, Simon Harding, Mikhail Frank, Gabriele Spina, Alexander Förster, Jochen Triesch, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


In this work we introduce a technique for a humanoid robot to autonomously learn the representations of objects within its visual environment. Our approach involves an attention mechanism in association with feature based segmentation that explores the environment and provides object samples for training. These samples are learned for further object identification using Cartesian Genetic Programming (CGP). The learned identification is able to provide robust and fast segmentation of the objects, without using features. We showcase our system and its performance on the iCub humanoid robot. © 2012 IEEE.
Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL 2012
StatePublished - Dec 1 2012
Externally publishedYes


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