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

J. Leitner, P. Chandrashekhariah, S. Harding, M. Frank, G. Spina, A. Förster, J. Triesch, J. Schmidhuber

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

4 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Development and Learning, and Epigenetic Robotics (ICDL), 7-9 November 2012, San Diego
Place of PublicationSan Diego
DOIs
Publication statusPublished - 2012
Eventconference; IEEE Conference on Development and Learning, and Epigenetic Robotics; 2012-11-07; 2012-11-09 -
Duration: 7 Nov 20129 Nov 2012

Conference

Conferenceconference; IEEE Conference on Development and Learning, and Epigenetic Robotics; 2012-11-07; 2012-11-09
Period7/11/129/11/12
OtherIEEE Conference on Development and Learning, and Epigenetic Robotics

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