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Generating and Adapting Probabilistic Movement Primitives

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

Abstract

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.

References

  1. 1.
    Abdolmaleki, A., Simões, D., Lau, N., Reis, L.P., Neumann, G.: Contextual relative entropy policy search with covariance matrix adaptation. In: 2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 94–99 (2016)Google Scholar
  2. 2.
    Bishop, C.: Pattern Recognition and Machine Learning. Oxford Pergamon Press (1967)Google Scholar
  3. 3.
    Calinon, S.: A tutorial on task-parameterized movement learning and retrieval. Intell. Serv. Robot. 9(1), 1–29 (2016)CrossRefGoogle Scholar
  4. 4.
    Calinon, S., D’halluin, F., Sauser, E.L., Caldwell, D.G., Billard, A.G.: Learning and reproduction of gestures by imitation. IEEE Robot. Autom. Mag. 17(2), 44–54 (2010)CrossRefGoogle Scholar
  5. 5.
    Colomé, A., Neumann, G., Peters, J., Torras, C.: Dimensionality reduction for probabilistic movement primitives. In: IEEE-RAS 14th International Conference on Humanoid Robots (Humanoids), pp. 794–800 (2014)Google Scholar
  6. 6.
    Deisenroth, M.P., Neumann, G., Peters, J.: A survey on policy search for robotics. Found. Trends Robot. 2(1–2), 1–142 (2013)Google Scholar
  7. 7.
    Edelsbrunner, H., Harer, J.L.: Computational Topology: An Introduction. American Mathematical Society (2009)Google Scholar
  8. 8.
    Fabisch, A., Metzen, J.H.: Active contextual policy search. J. Mach. Learn. Res. 15, 3371–3399 (2014)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Ijspeert, A.J., Nakanishi, J., Hoffmann, H., Pastor, P., Schaal, S.: Dynamical movement primitives: learning attractor models for motor behaviors. Neural Comput. 25(2), 328–373 (2013)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Khansari-Zadeh, S.M., Billard, A.: Learning stable nonlinear dynamical systems with Gaussian mixture models. IEEE Trans. Robot. 27(5), 943–957 (2011)CrossRefGoogle Scholar
  11. 11.
    Koert, D., Maedam, G., Lioutikov, R., Neumann, G., Peters, J.: Demonstration based trajectory optimization for generalizable robot motions. In: IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 515–522 (2016)Google Scholar
  12. 12.
    Kupcsik, A.G., Deisenroth, M.P., Peters, J., Neumann, G.: Data-efficient generalization of robot skills with contextual policy search. In: AAAI Conference on Artificial Intelligence, pp. 1401–1407 (2013)Google Scholar
  13. 13.
    Lofberg, J.: YALMIP matlab library. https://yalmip.github.io/download/
  14. 14.
    Paraschos, A., Daniel, C., Peters, J., Neumann, G.: Probabilistic movement primitives. In: Advances in Neural Information Processing Systems (NIPS), pp. 2616–2624 (2013)Google Scholar
  15. 15.
    Pastor, P., Righetti, L., Kalakrishnan, M., Schaal, S.: Online movement adaptation based on previous sensor experiences. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 365–371 (2011)Google Scholar
  16. 16.
    Peters, J., Mülling, K., Altün, Y.: Relative entropy policy search. In: AAAI Conference on Artificial Intelligence, pp. 1607–1612 (2010)Google Scholar
  17. 17.
    Pignat, E., Calinon, S.: Learning adaptive dressing assistance from human demonstration. Robot. Auton. Syst. 93, 61–75 (2017)CrossRefGoogle Scholar
  18. 18.
    Stulp, F., Oztop, E., Pastor, P., Beetz, M., Schaal, S.: Compact models of motor primitive variations for predictable reaching and obstacle avoidance. In 2009 9th IEEE-RAS International Conference on Humanoid Robots, pp. 589–595 (2009)Google Scholar
  19. 19.
    Tausz, A., Vejdemo-Johansson, M., Adams, H.: Javaplex: a research software package for persistent (co)homology. In: International Conference on Mathematical Software, pp. 129–136 (2014)Google Scholar
  20. 20.
    Toussaint, M.: Lecture notes: Gaussian identities. http://ipvs.informatik.uni-stuttgart.de/mlr/marc/notes/gaussians.pdf

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

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

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