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 PaperResearchpeer-review

5 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 publication2012 IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL 2012
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages186-191
Number of pages6
ISBN (Print)9781467349635
DOIs
Publication statusPublished - 2012
Externally publishedYes
EventIEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL) 2012 - San Diego, United States of America
Duration: 7 Nov 20129 Nov 2012
https://ieeexplore.ieee.org/xpl/conhome/6384412/proceeding (Proceedings)

Conference

ConferenceIEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL) 2012
Abbreviated titleICDL 2012
Country/TerritoryUnited States of America
CitySan Diego
Period7/11/129/11/12
Internet address

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