Rueckert Elmar
Rueckert Elmar
Assistant Professor, ROB, Universität zu Lübeck
Verified email at rob.uni-luebeck.de - Homepage
Title
Cited by
Cited by
Year
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
462013
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
452015
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
422012
Recurrent spiking networks solve planning tasks
E Rueckert, D Kappel, D Tanneberg, D Pecevski, J Peters
Scientific reports 6, 21142, 2016
382016
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
302016
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
292015
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
212016
Simultaneous localisation and mapping for mobile robots with recent sensor technologies
EA Rückert
na, 2009
182009
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
172017
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
142015
Stochastic optimal control methods for investigating the power of morphological computation
EA Rückert, G Neumann
Artificial Life 19 (1), 115-131, 2013
142013
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
102019
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
102014
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
72016
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
72016
Vroegmoderne economische ontwikkeling en sociale repercussies in de zuidelijke Nederlanden
W Ryckbosch
tijdschrift voor sociale en economische geschiedenis 7 (3), 26-55, 2010
62010
Experience reuse with probabilistic movement primitives
S Stark, J Peters, E Rueckert
arXiv preprint arXiv:1908.03936, 2019
52019
Probabilistic movement primitives under unknown system dynamics
A Paraschos, E Rueckert, J Peters, G Neumann
Advanced Robotics 32 (6), 297-310, 2018
52018
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
52016
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
52015
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