Generating and Adapting Probabilistic Movement Primitives

  • Adrià ColoméEmail author
  • Carme Torras
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 134)


In this chapter we propose a framework for easily initializing ProMPs with synthetic data, and building a conditioning dataset in order to map context variables to motion parameters, which can be used for both exploiting its features by executing such contextualized trajectories, or improving through contextual PS. This method is combined with a stochastic-based obstacle avoidance that allows to modify these trajectories. Moreover, this chapter presents generative mixture models of ProMPs, which can be built from data that may include different actions.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institut de Robòtica i Informàtica Industrial (UPC-CSIC)BarcelonaSpain

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