Elmar Rückert
Research output
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
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
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
- 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
- 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
- Published
Learning inverse dynamics models in O(n) time with LSTM networks
Rueckert, E., Nakatenus, M., Tosatto, S. & Peters, J., 22 Dec 2017, IEEE-RAS International Conference on Humanoid Robots. p. 811-816 6 p. (IEEE-RAS International Conference on Humanoid Robots).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Inverse reinforcement learning via nonparametric spatio-temporal subgoal modeling
Šošić, A., Rueckert, E., Peters, J., Zoubir, A. M. & Koeppl, H., 1 Oct 2018, In: Journal of Machine Learning Research. 19.2018, 69, 45 p.Research output: Contribution to journal › Article › Research › peer-review
- Published
A comparison of distance measures for learning nonparametric motor skill libraries
Stark, S., Peters, J. & Rueckert, E., 22 Dec 2017, IEEE-RAS International Conference on Humanoid Robots. p. 624-630 7 p. (IEEE-RAS International Conference on Humanoid Robots).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
- Published
Experience Reuse with Probabilistic Movement Primitives
Stark, S., Peters, J. & Rueckert, E., Nov 2019, IEEE International Conference on Intelligent Robots and Systems. p. 1210-1217 8 p. (IEEE International Conference on Intelligent Robots and Systems).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
- Published
SKID RAW: Skill Discovery from Raw Trajectories
Tanneberg, D., Ploeger, K., Rueckert, E. & Peters, J., Jul 2021, In: IEEE robotics and automation letters. 6, 3, p. 4696-4703 8 p., 9387162.Research output: Contribution to journal › Article › Research › peer-review
