Online prediction of robot to human handover events using vibrations

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

2 Citations (Scopus)
27 Downloads (Pure)


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
Number of pages5
ISBN (Electronic)9789082797015
Publication statusPublished - 3 Dec 2018
Event26th European Signal Processing Conference - Rome, Italy
Duration: 3 Sept 20187 Sept 2018


Conference26th European Signal Processing Conference
Abbreviated titleEUSIPCO 2018
Internet address


  • autonomous
  • handover events
  • machine learning
  • neural networks


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