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Jürgen ’Juxi’ Leitner
          S. Harding, M. Frank, A. Förster, J. Schmidhuber

          istituto dalle molle di studi sull’intelligenza artificiale
                                 università della svizzera italiana
                                                   idsia / usi / supsi




              transferring
        spatial perception
between robots operating
   in a shared workspace
                                                         #iros 2012
our iCub
 setup is for
manipulation
visual perception




Harding et al., GPTP 2012, Leitner et al., ICDL 2012   IROS 2012
current
localisation approaches
learning
    approach


+
transferring
spatial perception
COLLISIONS ARE
         BAD
MoBeE
framework
            Frank et al., ICINCO, 2012.
MoBeE
framework
            Frank et al., ICINCO, 2012.
9DOF              bounding box
                                  iCub               6 per eye
                                                        .
                                                        .
                                                        .




                                         fully connected
                                              .
                                              .
                                              .




                                         fully connected




                                           Cartesian
                                          Coordinates
            spatial perception
neural network
600                                                       400                                                        -200
             550                                                       300                                                        -300

             500                                                       200                                                        -400

             450                                                       100                                                        -500
                                                                         0                                                        -600
             400




                                                              Y (mm)
    X (mm)




                                                                                                                         Z (mm)
                                                                       -100                                                       -700
             350
                                                                       -200                                                       -800
             300
                                                                       -300                                                       -900
             250
                                                                       -400                                                  -1000
             200
                                                                       -500                                                  -1100
             150                                                       -600                               Predicted          -1200
                                                                                                          Expected
             100                                                       -700                                                  -1300
                   0   200    400      600      800    1000                   0   200   400       600    800      1000                   0
                                Sample Index                                              Sample Index
         -200
         -300
         -400
         -500
         -600
Z (mm)




         -700
         -800


    -1000
         -900
                                                                                                   results
                                                                                                     ANN
    -1100
    -1200
    -1300
                   0   200   400      600      800    1000
                               Sample Index
spatial perception
neural network
future
 work

 unsupervised learning
 different spatial representations
 “intrinsic motivation” learning
 online, continuous learning
 eye-hand coordination
thanks
                                           for listening
       juxi@idsia.ch   http://Juxi.net/projects
video at http://Juxi.net/projects/im-clever/year3.html

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Transferring Spatial Perception Between Robots Operating In A Shared Workspace

  • 1. Jürgen ’Juxi’ Leitner S. Harding, M. Frank, A. Förster, J. Schmidhuber istituto dalle molle di studi sull’intelligenza artificiale università della svizzera italiana idsia / usi / supsi transferring spatial perception between robots operating in a shared workspace #iros 2012
  • 2. our iCub setup is for manipulation
  • 3. visual perception Harding et al., GPTP 2012, Leitner et al., ICDL 2012 IROS 2012
  • 5. learning approach +
  • 8. MoBeE framework Frank et al., ICINCO, 2012.
  • 9. MoBeE framework Frank et al., ICINCO, 2012.
  • 10. 9DOF bounding box iCub 6 per eye . . . fully connected . . . fully connected Cartesian Coordinates spatial perception neural network
  • 11. 600 400 -200 550 300 -300 500 200 -400 450 100 -500 0 -600 400 Y (mm) X (mm) Z (mm) -100 -700 350 -200 -800 300 -300 -900 250 -400 -1000 200 -500 -1100 150 -600 Predicted -1200 Expected 100 -700 -1300 0 200 400 600 800 1000 0 200 400 600 800 1000 0 Sample Index Sample Index -200 -300 -400 -500 -600 Z (mm) -700 -800 -1000 -900 results ANN -1100 -1200 -1300 0 200 400 600 800 1000 Sample Index
  • 13. future work unsupervised learning different spatial representations “intrinsic motivation” learning online, continuous learning eye-hand coordination
  • 14. thanks for listening juxi@idsia.ch http://Juxi.net/projects video at http://Juxi.net/projects/im-clever/year3.html