Autonomous Robots

, Volume 42, Issue 3, pp 529–551 | Cite as

Using probabilistic movement primitives in robotics

  • Alexandros ParaschosEmail author
  • Christian Daniel
  • Jan Peters
  • Gerhard Neumann


Movement Primitives are a well-established paradigm for modular movement representation and generation. They provide a data-driven representation of movements and support generalization to novel situations, temporal modulation, sequencing of primitives and controllers for executing the primitive on physical systems. However, while many MP frameworks exhibit some of these properties, there is a need for a unified framework that implements all of them in a principled way. In this paper, we show that this goal can be achieved by using a probabilistic representation. Our approach models trajectory distributions learned from stochastic movements. Probabilistic operations, such as conditioning can be used to achieve generalization to novel situations or to combine and blend movements in a principled way. We derive a stochastic feedback controller that reproduces the encoded variability of the movement and the coupling of the degrees of freedom of the robot. We evaluate and compare our approach on several simulated and real robot scenarios.


Imitation learning Movement primitives Trajectory representation Control Robotics 



The research leading to these results has received funding from the European Community’s Framework Programme CoDyCo (FP7-ICT-2011-9 Grant No. 600716), CompLACS (FP7-ICT-2009-6 Grant No. 270327), GeRT (FP7-ICT-2009-4 Grant No. 248273), and ERC StG SKILLS4ROBOTS.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Alexandros Paraschos
    • 1
    Email author
  • Christian Daniel
    • 2
  • Jan Peters
    • 1
    • 3
  • Gerhard Neumann
    • 4
  1. 1.Technische Universität DarmstadtDarmstadtGermany
  2. 2.Bosch Center for Artificial IntelligenceRenningenGermany
  3. 3.Max-Planck-Institut für Intelligente SystemeTübingenGermany
  4. 4.Computational Learning for Autonomous SystemsSchool of Computer Science, University of LincolnLincolnUK

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