#ijcnn 2013
ANNs for
spatial perception
towards visual object localisation in humanoids
S. Harding, M. Frank,A. Förster, J...
manipulation
our iCub
setup is for
object manipulation
towards learning
http://robotics.idsia.ch/
MoBeE
framework Frank et al., 2012.
MoBeE
generation
motion
Stollenga et al, 2013
Shakey2013Winner
perception
visual
thanks to G. Metta and IIT for this picture
objects
detecting
Harding et al., 2013
Leitner et al., ICDL 2012,ARS 2012, BICA 2012, CEC 2013
localisation approaches
current
approach
learning
=>
transferring
spatial perception
setup
learning
trainingset
9DOF
iCub
bounding box
6 per eye
Cartesian
Coordinates
.
.
.
spatial perception
neural network
.
.
.
9DOF
iCub
boundingbox
6pereye
Cartesian
Coordinates
fullyconnected
fullyconnected
...
hidden layer
neurons
results
ANN-1300
-1200
-1100
-1000
-900
-800
-700
-600
-500
-400
-300
-200
0 200 400 600 800 1000
Z(mm)
Sample Index
-700
...
results
ANN
work
future
eye-hand coordination
different spatial representations
online, continuous learning
for listening
thanks
juxi@idsia.ch http://Juxi.net/projects
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Artificial Neural Networks For Spatial Perception: Towards Visual Object Localisation in Humanoid Robots #ijcnn2013

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Presentation at the 2013 IJCNN (Int'l Joint Conference for Neural Networks) of our paper.
More info http://Juxi.net/

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Artificial Neural Networks For Spatial Perception: Towards Visual Object Localisation in Humanoid Robots #ijcnn2013

  1. 1. #ijcnn 2013 ANNs for spatial perception towards visual object localisation in humanoids S. Harding, M. Frank,A. Förster, J. Schmidhuber istituto dalle molle di studi sull’intelligenza artificiale università della svizzera italiana idsia / usi / supsi Jürgen ’Juxi’ Leitner
  2. 2. manipulation our iCub setup is for
  3. 3. object manipulation towards learning http://robotics.idsia.ch/
  4. 4. MoBeE framework Frank et al., 2012. MoBeE
  5. 5. generation motion Stollenga et al, 2013 Shakey2013Winner
  6. 6. perception visual thanks to G. Metta and IIT for this picture
  7. 7. objects detecting Harding et al., 2013 Leitner et al., ICDL 2012,ARS 2012, BICA 2012, CEC 2013
  8. 8. localisation approaches current approach learning =>
  9. 9. transferring spatial perception
  10. 10. setup learning
  11. 11. trainingset 9DOF iCub bounding box 6 per eye Cartesian Coordinates . . .
  12. 12. spatial perception neural network . . . 9DOF iCub boundingbox 6pereye Cartesian Coordinates fullyconnected fullyconnected . . .
  13. 13. hidden layer neurons
  14. 14. results ANN-1300 -1200 -1100 -1000 -900 -800 -700 -600 -500 -400 -300 -200 0 200 400 600 800 1000 Z(mm) Sample Index -700 -600 -500 -400 -300 -200 -100 0 100 200 300 400 0 200 400 600 800 1000 Y(mm) Sample Index Predicted Expected 100 150 200 250 300 350 400 450 500 550 600 0 200 400 600 800 1000 X(mm) Sample Index -1300 -1200 -1100 -1000 -900 -800 -700 -600 -500 -400 -300 -200 0 Z(mm)
  15. 15. results ANN
  16. 16. work future eye-hand coordination different spatial representations online, continuous learning
  17. 17. for listening thanks juxi@idsia.ch http://Juxi.net/projects

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