Online prediction of robot to human handover events using vibrations

Harmeet Singh, Marco Controzzi, Christian Cipriani, Gaetano Di Caterina, Lykourgos Petropoulakis, John Soraghan

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Abstract

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.
Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference (EUSIPCO)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages687-691
Number of pages5
ISBN (Electronic)9789082797015
DOIs
Publication statusPublished - 3 Dec 2018
Event26th European Signal Processing Conference - Rome, Italy
Duration: 3 Sep 20187 Sep 2018
http://www.eusipco2018.org/index.php

Conference

Conference26th European Signal Processing Conference
Abbreviated titleEUSIPCO 2018
CountryItaly
CityRome
Period3/09/187/09/18
Internet address

Keywords

  • autonomous
  • handover events
  • machine learning
  • neural networks

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  • Cite this

    Singh, H., Controzzi, M., Cipriani, C., Di Caterina, G., Petropoulakis, L., & Soraghan, J. (2018). Online prediction of robot to human handover events using vibrations. In 2018 26th European Signal Processing Conference (EUSIPCO) (pp. 687-691). IEEE. https://doi.org/10.23919/EUSIPCO.2018.8553474