Kühn, JanJanKühnJäger, JoasJoasJägerKuhl, MatthiasMatthiasKuhlManoli, YiannosYiannosManoli2020-12-042020-12-042019MikroSystemTechnik Kongress, Mikroelektronik (MEMS 2019)http://hdl.handle.net/11420/8129Tactile fingers provide robotic gripping systems with essential sensor feedback for the manipulation of objects. The presented tactile sensor finger measures static forces and slip vibrations with an array of identical stress sensors. Instead of calibrating the sensor to measure forces quantitatively, machine learning is used to extract abstract information from the raw data of the sensors. This information processing scheme can classify the direction of applied forces with an accuracy of 99.8 % through the spatial distribution of stress sensors. At the same time, with the same signal processing, slip can be detected without spectral analysis with an accuracy of 99.6 %.deConference PaperOther