2010 | OriginalPaper | Buchkapitel
Learning and Prediction of Soft Object Deformation Using Visual Analysis of Robot Interactions
verfasst von : Ana-Maria Cretu, Pierre Payeur, Emil M. Petriu
Erschienen in: Advances in Visual Computing
Verlag: Springer Berlin Heidelberg
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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.