Názov:Biologically inspired predictive model of proprioceptive body representations
Vedúci: prof. Ing. Igor Farkaą, Dr.
Kµúčové slová:body schema, somatosensory, iCub, predictive model, population coding
Abstrakt:The goal of this thesis is to create a simplified, biologically inspired multimodal representation of the body (body schema). For this we use the open simulator of the humanoid robot iCub that learns to associate two modalities of somatosensation - proprioception and tactile sensation. The proposed solution employs a predictive model based on a multilayer perceptron that is trained off-line in a supervised way, given the current proprioceptive state and an action as inputs, to predict the future body state and a potential tactile sensation. Prior to training the model, the training data was collected from the simulated iCub that was made to perform various types of the self-touch using its both arms. The neural network model was successfully tested for its ability to predict touch in the corresponding proprioceptive states, when no action was implied as input, but also to anticipate the touch during action, shortly before it occurred.

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