| 3D shape context and distance transform for action recognition M Grundmann, F Meier, I Essa 2008 19th International Conference on Pattern Recognition, 1-4, 2008 | 95 | 2008 |
| Movement segmentation using a primitive library F Meier, E Theodorou, F Stulp, S Schaal 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2011 | 83 | 2011 |
| From dynamic movement primitives to associative skill memories P Pastor, M Kalakrishnan, F Meier, F Stulp, J Buchli, E Theodorou, ... Robotics and Autonomous Systems 61 (4), 351-361, 2013 | 68 | 2013 |
| Se3-pose-nets: Structured deep dynamics models for visuomotor control A Byravan, F Leeb, F Meier, D Fox 2018 IEEE International Conference on Robotics and Automation (ICRA), 3339-3346, 2018 | 57* | 2018 |
| Incremental local gaussian regression F Meier, P Hennig, S Schaal Advances in Neural Information Processing Systems, 972-980, 2014 | 56 | 2014 |
| Real-time perception meets reactive motion generation D Kappler, F Meier, J Issac, J Mainprice, CG Cifuentes, M Wüthrich, ... IEEE Robotics and Automation Letters 3 (3), 1864-1871, 2018 | 37 | 2018 |
| Movement segmentation and recognition for imitation learning F Meier, E Theodorou, S Schaal Artificial Intelligence and Statistics, 761-769, 2012 | 33 | 2012 |
| ProSAS: a database for analyzing alternative splicing in the context of protein structures F Birzele, R Küffner, F Meier, F Oefinger, C Potthast, R Zimmer Nucleic acids research 36 (suppl_1), D63-D68, 2007 | 31 | 2007 |
| Generalized inner loop meta-learning E Grefenstette, B Amos, D Yarats, PM Htut, A Molchanov, F Meier, D Kiela, ... arXiv preprint arXiv:1910.01727, 2019 | 28 | 2019 |
| Learning coupling terms for obstacle avoidance A Rai, F Meier, A Ijspeert, S Schaal 2014 IEEE-RAS International Conference on Humanoid Robots, 512-518, 2014 | 28 | 2014 |
| Towards robust online inverse dynamics learning F Meier, D Kappler, N Ratliff, S Schaal 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2016 | 26 | 2016 |
| Learning feedback terms for reactive planning and control A Rai, G Sutanto, S Schaal, F Meier 2017 IEEE International Conference on Robotics and Automation (ICRA), 2184-2191, 2017 | 25 | 2017 |
| Drifting gaussian processes with varying neighborhood sizes for online model learning F Meier, S Schaal 2016 IEEE International Conference on Robotics and Automation (ICRA), 264-269, 2016 | 23 | 2016 |
| Meta-learning via learned loss S Bechtle, A Molchanov, Y Chebotar, E Grefenstette, L Righetti, ... arXiv preprint arXiv:1906.05374, 2019 | 19* | 2019 |
| Doomed: Direct online optimization of modeling errors in dynamics N Ratliff, F Meier, D Kappler, S Schaal Big data 4 (4), 253-268, 2016 | 18 | 2016 |
| Efficient Bayesian local model learning for control F Meier, P Hennig, S Schaal 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2014 | 17 | 2014 |
| Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots. A Rai, R Antonova, F Meier, CG Atkeson J. Mach. Learn. Res. 20 (49), 1-24, 2019 | 16 | 2019 |
| Learning sensor feedback models from demonstrations via phase-modulated neural networks G Sutanto, Z Su, S Schaal, F Meier 2018 IEEE International Conference on Robotics and Automation (ICRA), 1142-1149, 2018 | 16 | 2018 |
| A probabilistic representation for dynamic movement primitives F Meier, S Schaal arXiv preprint arXiv:1612.05932, 2016 | 16 | 2016 |
| Locally Weighted Regression for Control. JA Ting, S Vijayakumar, S Schaal Encyclopedia of Machine Learning 11, 613-624, 2010 | 14 | 2010 |
Stefan SchaalComputer Scientist in Robotics and AIVerified email at google.com
