LAUSR

Tactile perception methods rely on creating mappings from tactile data to percepts. In many approaches to artificial tactile perception, this involves extensive sampling of the object during the training phase. We introduce here a method to instead generalize tactile features across different orientations. This method is applied to the TacTip v2, a three-dimensional printed optical tactile sensor with internal pins acting as taxels arranged with a 12-fold rotational symmetry. By rotating a small sample of tactile images, we are able to generalize tactile stimuli to new orientations. The method was validated across several tactile stimuli on an edge orientation classification task. Data were then generalized for a combination of orientations and locations of one of these stimuli, and this dataset was used as the basis for an exploratory control task: contour following around a circular disk. The generalization method leads to a strong reduction in the time needed to gather training data and only a moderate increase in classification error, and is particularly suited for multidimensional tactile data sampling in complex tasks such as tactile manipulation. We expect the method to generalize well to other tactile stimuli.