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Univ.-Prof. Dr. Elmar Rueckert
Univ.-Prof. Dr. Elmar Rueckert
Chair of Cyber-Physical-Systems at Montanuniversität Leoben
Zweryfikowany adres z ai-lab.science - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
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
882013
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
842017
Recurrent spiking networks solve planning tasks
E Rueckert, D Kappel, D Tanneberg, D Pecevski, J Peters
Scientific reports 6, 21142, 2016
842016
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
662016
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
662015
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
592012
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
482015
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
432016
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
322021
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
312019
Stochastic optimal control methods for investigating the power of morphological computation
EA Rückert, G Neumann
Artificial Life 19 (1), 115-131, 2013
302013
Probabilistic movement primitives under unknown system dynamics
A Paraschos, E Rueckert, J Peters, G Neumann
Advanced Robotics 32 (6), 297-310, 2018
252018
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
252015
Using deep reinforcement learning with automatic curriculum learning for mapless navigation in intralogistics
H Xue, B Hein, M Bakr, G Schildbach, B Abel, E Rueckert
Applied Sciences 12 (6), 3153, 2022
222022
Simultaneous localisation and mapping for mobile robots with recent sensor technologies
EA Rückert
na, 2009
222009
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), 28455, 2016
212016
Ros-mobile: An android application for the robot operating system
N Rottmann, N Studt, F Ernst, E Rueckert
arXiv preprint arXiv:2011.02781, 2020
192020
Inverse reinforcement learning via nonparametric spatio-temporal subgoal modeling
A Šošić, E Rueckert, J Peters, AM Zoubir, H Koeppl
Journal of Machine Learning Research 19 (69), 1-45, 2018
172018
Predictive exoskeleton control for arm-motion augmentation based on probabilistic movement primitives combined with a flow controller
M Jamšek, T Kunavar, U Bobek, E Rueckert, J Babič
IEEE robotics and automation letters 6 (3), 4417-4424, 2021
162021
Multimodal visual-tactile representation learning through self-supervised contrastive pre-training
V Dave, F Lygerakis, E Rueckert
arXiv preprint arXiv:2401.12024, 2024
152024
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