Univ.-Prof. Dr. Elmar Rueckert
Univ.-Prof. Dr. Elmar Rueckert
Chair of Cyber-Physical-Systems at Montanuniversität Leoben
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Cited by
Cited by
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
Recurrent spiking networks solve planning tasks
E Rueckert, D Kappel, D Tanneberg, D Pecevski, J Peters
Scientific reports 6, 21142, 2016
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
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
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
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
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
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
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
Simultaneous localisation and mapping for mobile robots with recent sensor technologies
EA Rückert
na, 2009
Stochastic optimal control methods for investigating the power of morphological computation
EA Rückert, G Neumann
Artificial Life 19 (1), 115-131, 2013
Probabilistic movement primitives under unknown system dynamics
A Paraschos, E Rueckert, J Peters, G Neumann
Advanced Robotics 32 (6), 297-310, 2018
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
Inverse reinforcement learning via nonparametric spatio-temporal subgoal modeling
A Šošic, AM Zoubir, E Rueckert, J Peters, H Koeppl
Journal of Machine Learning Research 19 (69), 1-45, 2018
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
Skid raw: Skill discovery from raw trajectories
D Tanneberg, K Ploeger, E Rueckert, J Peters
IEEE Robotics and Automation Letters 6 (3), 4696-4703, 2021
Experience reuse with probabilistic movement primitives
S Stark, J Peters, E Rueckert
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019
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
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
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