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Humanoid Learns to Detect Its Own Hands #cec2013

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#cec2013
humanoid learns
to detect its own hands
S. Harding, M. Frank,
A. Förster, J. Schmidhuber
idsia / usi / supsi, mac...
manipulation
our iCub
setup is for
SOLVED”
playing
CHESS is
“

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Humanoid Learns to Detect Its Own Hands #cec2013

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My presentation at the Congress on Evolutionary Computation (CEC) 2013 in Cancun, Mexico.

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.

My presentation at the Congress on Evolutionary Computation (CEC) 2013 in Cancun, Mexico.

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.

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Humanoid Learns to Detect Its Own Hands #cec2013

  1. 1. #cec2013 humanoid learns to detect its own hands S. Harding, M. Frank, A. Förster, J. Schmidhuber idsia / usi / supsi, machine intelligence ltd Jürgen ’Juxi’ Leitner
  2. 2. manipulation our iCub setup is for
  3. 3. SOLVED” playing CHESS is “
  4. 4. perception visual thanks to G. Metta and IIT for this picture
  5. 5. objects detecting Harding et al., GPTP 2012
  6. 6. cv approaches current
  7. 7. coordination hand-eye
  8. 8. visual hand detection
  9. 9. approach learningour
  10. 10. evolutionary computing
  11. 11. cartesian genetic programming + mindilate avgINP INP INP
  12. 12. + mindilate avgINP INP INP 3" "#2" "#1" 4.3" """" Func,on" Connec,on"1" Connec,on"2" A"real"number" cartesian genetic programming
  13. 13. icImage NaoFingers::runFilter() { ! icImage node0 = InputImages[3].min(InputImages[4]); ! icImage node1 = InputImages[1].unsharpen(11); ! icImage node2 = node0.Max(); ! icImage node3 = node0.gauss(7, -7); ! icImage node4 = node2.addc(6.71329411864281); ! icImage node5 = InputImages[0].mul(node1); ! icImage node6 = node3.dilate(6); ! icImage node7 = node5.sobely(15); ! icImage node9 = node4.absdiff(node6); ! icImage node10 = InputImages[5]; ! icImage node13 = node10.unsharpen(15); ! icImage node14 = node7.dilate(6); ! icImage node17 = node9.gauss(11); ! icImage node18 = node17.SmoothBlur(11); ! icImage node19 = node13.min(node14); ! icImage node27 = node19.SmoothBlur(11); ! icImage node31 = node18.Exp(); ! icImage node32 = node31.erode(4); ! icImage node37 = node27.mul(node32); ! icImage node38 = node37.ShiftDown(); ! icImage node41 = node38.ShiftDown(); ! icImage node49 = node41.laplace(19); ! return node49; } detection
  14. 14. detection
  15. 15. hands detecting
  16. 16. detect
  17. 17. detect
  18. 18. coordination hand-eye
  19. 19. for listening thanks juxi@idsia.ch http://Juxi.net/projects more infos at http://robotics.idsia.ch/im-clever/
  20. 20. for listening juxi@idsia.ch http://Juxi.net/projects more infos at http://robotics.idsia.ch/im-clever/ thanks

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