This document discusses using artificial neural networks for spatial perception in humanoid robots. The researchers trained an ANN using a dataset of object locations captured from a robot's cameras. The ANN was able to accurately predict the 3D location of objects based on the camera input. Future work includes improving eye-hand coordination and developing different spatial representations through continuous online learning.

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. manipulation
our iCub
setup is for

3. object manipulation
towards learning
http://robotics.idsia.ch/

4. MoBeE
framework Frank et al., 2012.
MoBeE

5. generation
motion
Stollenga et al, 2013
Shakey2013Winner

6. perception
visual
thanks to G. Metta and IIT for this picture

7. objects
detecting
Harding et al., 2013
Leitner et al., ICDL 2012,ARS 2012, BICA 2012, CEC 2013

8. localisation approaches
current
approach
learning
=>

9. transferring
spatial perception

10. setup
learning

11. trainingset
9DOF
iCub
bounding box
6 per eye
Cartesian
Coordinates
.
.
.

12. spatial perception
neural network
.
.
.
9DOF
iCub
boundingbox
6pereye
Cartesian
Coordinates
fullyconnected
fullyconnected
.
.
.

13. hidden layer
neurons

14. results
ANN-1300
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Z(mm)
Sample Index
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Y(mm)
Sample Index
Predicted
Expected
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X(mm)
Sample Index
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0
Z(mm)

15. results
ANN

16. work
future
eye-hand coordination
different spatial representations
online, continuous learning

17. for listening
thanks
juxi@idsia.ch http://Juxi.net/projects