Research output

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  1. 2016
  2. Published

    Learning probabilistic features from EMG data for predicting knee abnormalities

    Kohlschuetter, J., Peters, J. & Rueckert, E., 2016, IFMBE Proceedings. p. 662-666 5 p. (IFMBE Proceedings).

    Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research

  3. 2015
  4. Published

    Model-free Probabilistic Movement Primitives for physical interaction

    Paraschos, A., Rueckert, E., Peters, J. & Neumann, G., 11 Dec 2015, IEEE International Conference on Intelligent Robots and Systems. p. 2860-2866 7 p. (IEEE International Conference on Intelligent Robots and Systems).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  5. Published

    Extracting low-dimensional control variables for movement primitives

    Rueckert, E., Mundo, J., Paraschos, A., Peters, J. & Neumann, G., 29 Jun 2015, In: Proceedings / IEEE International Conference on Robotics and Automation. 2015-June, June, p. 1511-1518 8 p., 7139390.

    Research output: Contribution to journal › Conference article › peer-review

  6. Published

    Learning inverse dynamics models with contacts

    Calandra, R., Ivaldi, S., Deisenroth, M. P., Rueckert, E. & Peters, J., 29 Jun 2015, In: Proceedings / IEEE International Conference on Robotics and Automation. 2015-June, June, p. 3186-3191 6 p., 7139638.

    Research output: Contribution to journal › Conference article › peer-review

  7. Published

    Robust policy updates for stochastic optimal control

    Rueckert, E., Mindt, M., Peters, J. & Neumann, G., 12 Feb 2015, p. 388-393. 6 p.

    Research output: Contribution to conference › Paper › peer-review

  8. 2013

  9. Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems

    Rückert, E. & d'Avella, A., 17 Oct 2013, In: Frontiers in computational neuroscience. 7.2013, October, 18 p.

    Research output: Contribution to journal › Article › Research › peer-review

  10. Learned graphical models for probabilistic planning provide a new class of movement primitives

    Rückert, E. A., Neumann, G., Toussaint, M. & Maass, W., 2 Jan 2013, (E-pub ahead of print) In: Frontiers in computational neuroscience. 6.2013, January, 20 p., 97.

    Research output: Contribution to journal › Article › Research › peer-review

  11. Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation

    Rückert, E. A. & Neumann, G., Jan 2013, In: Artificial Life. 19.2013, 1, p. 115–131 17 p.

    Research output: Contribution to journal › Article › Research › peer-review

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