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Autonomous Robots

, Volume 41, Issue 3, pp 593–612 | Cite as

Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks

  • Guilherme J. MaedaEmail author
  • Gerhard Neumann
  • Marco Ewerton
  • Rudolf Lioutikov
  • Oliver Kroemer
  • Jan Peters
Article

Abstract

This paper proposes an interaction learning method for collaborative and assistive robots based on movement primitives. The method allows for both action recognition and human–robot movement coordination. It uses imitation learning to construct a mixture model of human–robot interaction primitives. This probabilistic model allows the assistive trajectory of the robot to be inferred from human observations. The method is scalable in relation to the number of tasks and can learn nonlinear correlations between the trajectories that describe the human–robot interaction. We evaluated the method experimentally with a lightweight robot arm in a variety of assistive scenarios, including the coordinated handover of a bottle to a human, and the collaborative assembly of a toolbox. Potential applications of the method are personal caregiver robots, control of intelligent prosthetic devices, and robot coworkers in factories.

Keywords

Movement primitives Physical human–robot interaction  Imitation learning Mixture model Action recognition  Trajectory generation 

Notes

Acknowledgments

The research leading to these results has received funding from the European Community’s Seventh Framework Programmes (FP7-ICT-2013-10) under Grant agreement #610878 (3rdHand) and from the European Union’s Horizon 2020 research and innovation programme under grant agreement #645582 (RoMaNS) and from the Project BIMROB of the Forum fr interdisziplinre Forschung (FiF) of the TU Darmstadt. The authors would like to acknowledge Heni Ben Amor for the invaluable ideas and discussions that contributed to this paper.

Supplementary material

Supplementary material 1 (mp4 124848 KB)

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Guilherme J. Maeda
    • 1
    Email author
  • Gerhard Neumann
    • 1
  • Marco Ewerton
    • 1
  • Rudolf Lioutikov
    • 1
  • Oliver Kroemer
    • 2
  • Jan Peters
    • 1
    • 3
  1. 1.Technische Universitaet DarmstadtDarmstadtGermany
  2. 2.University of Southern CaliforniaCaliforniaUSA
  3. 3.Max Planck Institute for Intelligent SystemsTuebingenGermany

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