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    A. Tulbure, Berthold Bäuml

    Superhuman Performance in Tactile Material Classification and Differentiation with a Flexible Pressure-Sensitive Skin

    Proceedings of the 2018 IEEE-RAS International Conference on Humanoid Robots (Humanoids 2018) [November 6-9, 2018; Beijing China]


    DOI: 10.1109/HUMANOIDS.2018.8624987

    Abstract anzeigen

    In this paper, we show that a robot equipped with a flexible and commercially available tactile skin can exceed human performance in the challenging tasks of material classification, i.e., uniquely identifying a given material by touch alone, and of material differentiation, i.e., deciding if the materials in a given pair of materials are the same or different. For processing the high dimensional spatio-temporal tactile signal, we use a new tactile deep learning network architecture TactNet-II which is based on TactNet [1] and is significantly extended with recently described architectural enhancements and training methods. TactN et- Iireaches an accuracy for the material classification task as high as 95.0 %. For the material differentiation a new Siamese network based architecture is presented which reaches an accuracy as high as 95.4 %. All the results have been achieved on a new challenging dataset of 36 everyday household materials. In a thorough human performance experiment with 15 subjects we show that the human performance is significantly lower than the robot's performance for both tactile tasks.