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We propose a neural arehilecture for the recognition of objects by haptia. We demonstrate its performance for a set of household objects and toys using a low cost 2D pressure sensor of coarse resolution, whicb is moved by a robot arm guided by contact points. The approach transfers the weU known viewbased method from compnler vision to the domain of tactile sensing. However, in contrast (0 computer vision, not static frames but entire time series of 2D prrssore profiles are evaluated.

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Abstract-We present a database of 2D pressure profile timeseries as a testbed for tactile object and surface recognition. The tactile database captures the surfaces of household and toy objects by moving a 2D pressure sensor mounted to an industrial robot arm around the objects using real-time trajectory calculation. Thus, it represents different "views" of the objects in a similar way as the well known Columbia Object Image Library (COIL) captures different views of an object by a camera. As a first application, objects in the database are classified using a neural network architecture.

No abstract