Abstract
We present Contour-net, a bio-inspired model for tactile contour-tracing driven by an Hopf oscillator. By controlling the rhythmic movements of a simulated insect-like feeler, the model executes both wide searching and local sampling movements. Contour-tracing is achieved by means of contact-induced phase-forwarding of the oscillator. To classify the shape of an object, collected contact events can be directly fed into machine learning algorithms with minimal pre-processing (scaling). Three types of classifiers were evaluated, the best one being a Support Vector Machine. The likelihood of correct classification steadily increases with the number of collected contacts, enabling an incremental classification during sampling. Given a sufficiently large training data set, tactile shape recognition can be achieved in a position-, orientation- and size-invariant manner. The suitability for robotic applications is discussed.
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Acknowledgements
This work was supported by EU grant EMICAB (FP7-ICT, grant no. 270182) to Prof. Volker Dürr. We thank Thierry Hoinville for many important suggestion regarding the model, and Holk Cruse for valuable comments on earlier versions of the manuscript.
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Appendix
The following link provides a zip archive that contains the Matlab scripts and data sets that were used to train the neural networks and support vector machines. Further, scripts are included to generate custom training data sets using the Contour-net method. http://www.andre-krause.net/publications/tcci15krause.zip
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Krause, A.F., Harischandra, N., Dürr, V. (2015). Shape Recognition Through Tactile Contour Tracing. In: Nguyen, N., Kowalczyk, R., Duval, B., van den Herik, J., Loiseau, S., Filipe, J. (eds) Transactions on Computational Collective Intelligence XX . Lecture Notes in Computer Science(), vol 9420. Springer, Cham. https://doi.org/10.1007/978-3-319-27543-7_3
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