Humanoid learns to detect its own hands

Jürgen Leitner, Simon Harding, Mikhail Frank, Alexander Förster, Jürgen Schmidhuber

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

12 Scopus citations


Robust object manipulation is still a hard problem in robotics, even more so in high degree-of-freedom (DOF) humanoid robots. To improve performance a closer integration of visual and motor systems is needed. We herein present a novel method for a robot to learn robust detection of its own hands and fingers enabling sensorimotor coordination. It does so solely using its own camera images and does not require any external systems or markers. Our system based on Cartesian Genetic Programming (CGP) allows to evolve programs to perform this image segmentation task in real-time on the real hardware. We show results for a Nao and an iCub humanoid each detecting its own hands and fingers. © 2013 IEEE.
Original languageEnglish (US)
Title of host publication2013 IEEE Congress on Evolutionary Computation, CEC 2013
Number of pages8
StatePublished - Aug 21 2013
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14


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