Autonomous Robots

, Volume 43, Issue 6, pp 1327–1342 | Cite as

A human inspired handover policy using Gaussian Mixture Models and haptic cues

  • Antonis Sidiropoulos
  • Efi Psomopoulou
  • Zoe DoulgeriEmail author
Part of the following topical collections:
  1. Special Issue: Learning for Human-Robot Collaboration


A handover strategy is proposed that aims at natural and fluent robot to human object handovers. For the approaching phase, a globally asymptotically stable dynamical system (DS) is utilized, trained from human demonstrations and exploiting the existence of mirroring in the human wrist motion. The DS operates in the robot task space thus achieving independence with respect to the robot platform, encapsulating the position and orientation of the human wrist within a single DS. It is proven that the motion generated by such a DS, having as target the current wrist pose of the receiver’s hand, is bounded and converges to the previously unknown handover location. Haptic cues based on load estimates at the robot giver ensure full object load transfer before grip release. The proposed strategy is validated with simulations and experiments in real settings.


Programming by Demonstration Gaussian Mixture Model Physical human-robot interaction Haptic communication 

Supplementary material

10514_2018_9705_MOESM1_ESM.mp4 (77.2 mb)
Supplementary material 1 (mp4 79014 KB)


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center for Research and Technology Hellas (CERTH)ThessalonikiGreece
  2. 2.Department of Electrical and Computer EngineeringAristotle University of ThessalonikiThessalonikiGreece

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