Online prediction of robot to human handover events using vibrations

Singh, Harmeet and Controzzi, Marco and Cipriani, Christian and Di Caterina, Gaetano and Petropoulakis, Lykourgos and Soraghan, John (2018) Online prediction of robot to human handover events using vibrations. In: 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, Piscataway, NJ, pp. 687-691. ISBN 9789082797015

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    One of the main issues for a robotic passer is to detect the onset of a handover, in order to avoid the object from being released when the human partner is not ready or if some impact occurs. This paper presents the methodology for a robotic passer, that is potentially able to estimate the interaction forces by the receiver on the object, thus to achieve fluent and safe handovers. The proposed system uses a vibrator that energizes the object and an accelerometer that monitors vibration propagation through the object during the handover. We focused on the machine-learning technique to classify between four states during object handover. A neural network was trained for these four states and tested online. In experimental trials an accuracy of 85.2% and 93.9% were obtained respectively for four classes and two classes of actions by a neural network classifier.