Learning and prediction of soft object deformation using visual analysis of robot interactions

Authors:
Ana-Maria Cretu;Pierre Payeur;Emil M. Petriu
Affiliations:
School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada;School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada;School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada
Venue:
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Year:
2010

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Abstract

The paper discusses an innovative approach to acquire and learn deformable objects' properties to allow the incorporation of soft objects in virtualized reality applications or the control of dexterous manipulators. Contours of deformable objects are tracked in a sequence of images collected from a camera and correlated to the interaction measurements gathered at the fingers of a robotic hand using a combination of unsupervised and supervised neural network architectures. The advantage of the proposed methodology is that it not only automatically and implicitly captures the real elastic behavior of an object regardless of its material, but it is also able to predict the shape of its contour for previously unseen interactions. The results obtained show that the proposed approach is fast, insensitive to slight changes in contrast and lighting, and able to model accurately and predict severe contour deformations.