Elmar Rueckert
Elmar Rueckert
Professor for Cyber-Physical-Systems
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Zitiert von
Zitiert von
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 (1), 21142, 2016
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
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
EA Rückert, G Neumann, M Toussaint, W Maass
Frontiers in computational neuroscience 6, 97, 2013
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
2015 IEEE International Conference on Robotics and Automation (ICRA), 1511-1518, 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
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
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
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
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
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
Ros-mobile: An android application for the robot operating system
N Rottmann, N Studt, F Ernst, E Rueckert
arXiv preprint arXiv:2011.02781, 2020
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
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
Experience reuse with probabilistic movement primitives
S Stark, J Peters, E Rueckert
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019
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