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KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]


Internal forward models are aimed to provide the system with the prediction of

changes in sensory observations as the consequent of its own actions. For the special

case where the sensed information is in the form of the camera images, the model

is called visual forward model. Images are one of the richest resources of data and

the ability to predict the sensory camera images, enables the robots to do more

autonomous and intelligent tasks. Most of actions performed by robots lead to

outcomes which are appearing in the vision system. Therefor the capability to

predict these outcomes in the form of images helps the robot to execute better long-

term plans. That is why the visual forward models are of particular importance.

The main challenges regarding the construction of the visual forward models are

the high amount of image data to be predicted and the degrees of freedom of the

robot's action which causes the complexities to grow rapidly.

In this work, we have investigated dierent methods to construct the visual forward

models for a robotic camera head setup. The forward model explores the contin-

gencies between the movements in the robot's neck and eye joints and the resulting

changes in the camera images. Four dierent methods to construct the visual for-

ward models are introduced and implemented. Learning of the forward models in

these methods is based on linear interpolation, radial basis function networks or

Gaussian processes given the correspondences between the successive frames ex-

tracted by the use of SURF descriptors or constructions of so-called cumulator

units. To examine the performance of the proposed methods, two dierent types

of experiments are designed with the dierence that in the rst experiment, depth

information is not relevant while in the second one it is. Our experimental results

show the success of the introduced methods in the construction of the visual forward

models also provide the weak and strong aspects of each method.

Place, publisher, year, edition, pages
National Category
Computer Science
URN: urn:nbn:se:kth:diva-142034OAI: diva2:699647
Educational program
Master of Science in Engineering - Computer Science and Technology
Available from: 2014-03-12 Created: 2014-02-28 Last updated: 2014-03-12Bibliographically approved

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