Rueckert Elmar
Rueckert Elmar
Assistant Professor, ROB, Universität zu Lübeck
Verified email at rob.uni-luebeck.de - Homepage
TitleCited byYear
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
402013
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
372012
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
342015
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
232016
Recurrent spiking networks solve planning tasks
E Rueckert, D Kappel, D Tanneberg, D Pecevski, J Peters
Scientific reports 6, 21142, 2016
232016
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
222015
Simultaneous localisation and mapping for mobile robots with recent sensor technologies
EA Rückert
na, 2009
162009
Stochastic optimal control methods for investigating the power of morphological computation
EA Rückert, G Neumann
Artificial Life 19 (1), 115-131, 2013
132013
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
112016
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
102015
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
82017
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
72014
Vroegmoderne economische ontwikkeling en sociale repercussies in de zuidelijke Nederlanden
W Ryckbosch
tijdschrift voor sociale en economische geschiedenis 7 (3), 26-55, 2010
72010
Low-cost sensor glove with force feedback for learning from demonstrations using probabilistic trajectory representations
E Rueckert, R Lioutikov, R Calandra, M Schmidt, P Beckerle, J Peters
arXiv preprint arXiv:1510.03253, 2015
62015
Probabilistic movement models show that postural control precedes and predicts volitional motor control
E Rueckert, J Čamernik, J Peters, J Babič
Scientific reports 6, 28455, 2016
52016
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
42016
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
32019
Probabilistic movement primitives under unknown system dynamics
A Paraschos, E Rueckert, J Peters, G Neumann
Advanced Robotics 32 (6), 297-310, 2018
32018
Online learning with stochastic recurrent neural networks using intrinsic motivation signals
D Tanneberg, J Peters, E Rueckert
Conference on Robot Learning, 167-174, 2017
32017
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
32016
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