Increasingly widespread available haptic sensors mounted on articulated hands offer new sensory channels that can complement shape extraction from vision to enable a more robust handling of objects in cases when vision is restricted or even unavailable. However, to estimate object shape from haptic interaction data is a difficult challenge due to the complexity of the contact interaction between the movable object and sensor surfaces, leading to a coupled estimation problem of shape and object

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... ose. While for vision efficient solutions to the underlying SLAM problem are known, the available information is much sparser in the tactile case, posing great difficulties for a straightforward adoption of standard SLAM algorithms. In the present paper, we thus explore whether a biologically inspired model based on dynamic neural fields can offer a route towards a practical algorithm for tactile SLAM. Our study is focused on a restricted scenario where a two-fingered robot hand manipulates an n-gon with a fixed rotational axis. We demonstrate that our model can accumulate shape information from reasonably short interaction sequences and autonomously build a representation despite significant ambiguity of the tactile data due to the rotational periodicity of the object. We conclude that the presented framework may be a suitable basis to solve the tactile SLAM problem also in more general settings which will be the focus of subsequent work.