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The paper presents a joint sensing strategy that combines tactile probing and range imaging for the mapping of the elastic properties that characterize 3D deformable objects. A feedforward neural network architecture is employed in an original manner to model the complex relationship between the surface deformation and the forces exemplified in non-rigid bodies. Experimental results are presented...
The paper discusses a novel unsupervised learning approach for tracking deformable objects manipulated by a robotic hand in a series of images collected by a video camera. The object of interest is automatically segmented from the initial frame in the sequence. The segmentation is treated as clustering based on color information and spatial features and an unsupervised network is employed to cluster...
This paper employs a neural gas network to obtain a compressed model of 3D geometry of objects, which accounts for elastic behavior as well. Based on the output of the network, we are able to cluster the object into areas of similar geometry and elasticity and then represent the elastic behavior of each cluster by an Elman neural network that models force-displacement behavior without explicit computation...
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