Neural network models of haptic shape perception

Magnus Johnsson, Christian Balkenius

Forskningsoutput: TidskriftsbidragArtikelPeer review

24 Citeringar (Scopus)

Sammanfattning

Three different models of tactile shape perception inspired by the human haptic system were tested using an 8 d.o.f. robot hand with 45 tactile sensors. One model is based on the tensor product of different proprioceptive and tactile signals and a self-organizing map (SOM). The two other models replace the tensor product operation with a novel self-organizing neural network, the Tensor-Multiple Peak Self-Organizing Map (T-MPSOM). The two T-MPSOM models differ in the procedure employed to calculate the neural activation. The computational models were trained and tested with a set of objects consisting of hard spheres, blocks and cylinders. All the models learned to map different shapes to different areas of the SOM, and the tensor product model as well as one of the T-MPSOM models also learned to discriminate individual test objects.

OriginalspråkEngelska
Sidor (från-till)720-727
Antal sidor8
TidskriftRobotics and Autonomous Systems
Volym55
Nummer9
DOI
StatusPublicerad - 2007-sep.-30

Fingeravtryck

Fördjupa i forskningsämnen för ”Neural network models of haptic shape perception”. Tillsammans bildar de ett unikt fingeravtryck.

Citera det här