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Univ.-Prof. Dr. Elmar Rueckert
Univ.-Prof. Dr. Elmar Rueckert
Chair of Cyber-Physical-Systems at Montanuniversität Leoben
Email yang diverifikasi di ai-lab.science - Beranda
Judul
Dikutip oleh
Dikutip oleh
Tahun
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
Recurrent spiking networks solve planning tasks
E Rueckert, D Kappel, D Tanneberg, D Pecevski, J Peters
Scientific reports 6, 21142, 2016
852016
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
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
672016
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
602012
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
332021
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
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
222016
Simultaneous localisation and mapping for mobile robots with recent sensor technologies
EA Rückert
na, 2009
212009
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
182018
Multimodal visual-tactile representation learning through self-supervised contrastive pre-training
V Dave, F Lygerakis, E Rueckert
2024 IEEE International Conference on Robotics and Automation (ICRA), 8013-8020, 2024
172024
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
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