Humanoid learns to detect its own hands

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

doi:10.1109/CEC.2013.6557729

Humanoid learns to detect its own hands

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Scopus citations

Abstract

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 language	English (US)
Title of host publication	2013 IEEE Congress on Evolutionary Computation, CEC 2013
Pages	1411-1418
Number of pages	8
DOIs	https://doi.org/10.1109/CEC.2013.6557729
State	Published - Aug 21 2013
Externally published	Yes

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10.1109/CEC.2013.6557729

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title = "Humanoid learns to detect its own hands",

abstract = "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. {\textcopyright} 2013 IEEE.",

author = "J{\"u}rgen Leitner and Simon Harding and Mikhail Frank and Alexander F{\"o}rster and J{\"u}rgen Schmidhuber",

note = "Generated from Scopus record by KAUST IRTS on 2022-09-14",

year = "2013",

month = aug,

day = "21",

doi = "10.1109/CEC.2013.6557729",

language = "English (US)",

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T1 - Humanoid learns to detect its own hands

AU - Leitner, Jürgen

AU - Harding, Simon

AU - Frank, Mikhail

AU - Förster, Alexander

AU - Schmidhuber, Jürgen

N1 - Generated from Scopus record by KAUST IRTS on 2022-09-14

PY - 2013/8/21

Y1 - 2013/8/21

N2 - 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.

AB - 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.

UR - http://ieeexplore.ieee.org/document/6557729/

UR - http://www.scopus.com/inward/record.url?scp=84881574097&partnerID=8YFLogxK

U2 - 10.1109/CEC.2013.6557729

DO - 10.1109/CEC.2013.6557729

M3 - Conference contribution

SN - 9781479904549

SP - 1411

EP - 1418

BT - 2013 IEEE Congress on Evolutionary Computation, CEC 2013

ER -

Humanoid learns to detect its own hands

Abstract

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