Rueckert Elmar
Rueckert Elmar
Assistant Professor, ROB, Universität zu Lübeck
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Zitiert von
Zitiert von
Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems
E Rückert, A d'Avella
Frontiers in computational neuroscience 7, 138, 2013
Learning inverse dynamics models with contacts
R Calandra, S Ivaldi, MP Deisenroth, E Rueckert, J Peters
2015 IEEE International Conference on Robotics and Automation (ICRA), 3186-3191, 2015
Learned Graphical Models for Probabilistic Planning Provide a New Class of Movement Primitives
E Rückert, G Neumann, M Toussaint, W Maass
Frontiers in Computational Neuroscience 6 (97), 2012
Recurrent spiking networks solve planning tasks
E Rueckert, D Kappel, D Tanneberg, D Pecevski, J Peters
Scientific reports 6, 21142, 2016
Learning soft task priorities for control of redundant robots
V Modugno, G Neumann, E Rueckert, G Oriolo, J Peters, S Ivaldi
2016 IEEE International Conference on Robotics and Automation (ICRA), 221-226, 2016
Extracting Low-Dimensional Control Variables for Movement Primitives
E Rueckert, J Mundo, A Paraschos, J Peters, G Neumann
Proc. IEEE Int. Conf. on Robotics and Automation (ICRA), 2015
A low-cost sensor glove with vibrotactile feedback and multiple finger joint and hand motion sensing for human-robot interaction
P Weber, E Rueckert, R Calandra, J Peters, P Beckerle
2016 25th IEEE International Symposium on Robot and Human Interactive …, 2016
Learning inverse dynamics models in o (n) time with lstm networks
E Rueckert, M Nakatenus, S Tosatto, J Peters
2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids …, 2017
Simultaneous localisation and mapping for mobile robots with recent sensor technologies
EA Rückert
na, 2009
Model-free probabilistic movement primitives for physical interaction
A Paraschos, E Rueckert, J Peters, G Neumann
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2015
Stochastic optimal control methods for investigating the power of morphological computation
EA Rückert, G Neumann
Artificial Life 19 (1), 115-131, 2013
Intrinsic motivation and mental replay enable efficient online adaptation in stochastic recurrent networks
D Tanneberg, J Peters, E Rueckert
Neural Networks 109, 67-80, 2019
Robust Policy Updates for Stochastic Optimal Control
E Rueckert, M Mindt, J Peters, G Neumann
Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2014
Model estimation and control of compliant contact normal force
M Azad, V Ortenzi, HC Lin, E Rueckert, M Mistry
2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids …, 2016
Deep spiking networks for model-based planning in humanoids
D Tanneberg, A Paraschos, J Peters, E Rueckert
2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids …, 2016
Probabilistic movement models show that postural control precedes and predicts volitional motor control
E Rueckert, J Čamernik, J Peters, J Babič
Scientific reports 6 (1), 1-12, 2016
Vroegmoderne economische ontwikkeling en sociale repercussies in de zuidelijke Nederlanden
W Ryckbosch
Tijdschrift voor sociale en Economische Geschiedenis 7 (3), 26-55, 2010
Experience reuse with probabilistic movement primitives
S Stark, J Peters, E Rueckert
arXiv preprint arXiv:1908.03936, 2019
Probabilistic movement primitives under unknown system dynamics
A Paraschos, E Rueckert, J Peters, G Neumann
Advanced Robotics 32 (6), 297-310, 2018
Online learning with stochastic recurrent neural networks using intrinsic motivation signals
D Tanneberg, J Peters, E Rueckert
Conference on Robot Learning, 167-174, 2017
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