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 | 86 | 2013 |
Recurrent spiking networks solve planning tasks E Rueckert, D Kappel, D Tanneberg, D Pecevski, J Peters Scientific reports 6, 21142, 2016 | 81 | 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 | 75 | 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 | 65 | 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 | 57 | 2012 |
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 | 56 | 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 | 45 | 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 | 42 | 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 | 31 | 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 | 25 | 2021 |
Stochastic optimal control methods for investigating the power of morphological computation EA Rückert, G Neumann Artificial Life 19 (1), 115-131, 2013 | 25 | 2013 |
Probabilistic movement primitives under unknown system dynamics A Paraschos, E Rueckert, J Peters, G Neumann Advanced Robotics 32 (6), 297-310, 2018 | 24 | 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 | 23 | 2015 |
Simultaneous localisation and mapping for mobile robots with recent sensor technologies EA Rückert na, 2009 | 21 | 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 | 20 | 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 | 18 | 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 | 17 | 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 | 16 | 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 | 14 | 2016 |
Experience reuse with probabilistic movement primitives S Stark, J Peters, E Rueckert 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019 | 12 | 2019 |