| Sample-efficient cross-entropy method for real-time planning C Pinneri, S Sawant, S Blaes, J Achterhold, J Stueckler, M Rolinek, ... Conference on Robot Learning, 1049-1065, 2021 | 87 | 2021 |
| Control what you can: Intrinsically motivated task-planning agent S Blaes, M Vlastelica Poganèiæ, J Zhu, G Martius Advances in Neural Information Processing Systems 32, 2019 | 37 | 2019 |
| Learning agile skills via adversarial imitation of rough partial demonstrations C Li, M Vlastelica, S Blaes, J Frey, F Grimminger, G Martius Conference on Robot Learning, 342-352, 2023 | 30 | 2023 |
| Few-shot learning in deep networks through global prototyping S Blaes, T Burwick Neural networks 94, 159-172, 2017 | 29 | 2017 |
| Electron density and plasma dynamics of a spherical theta pinch C Teske, Y Liu, S Blaes, J Jacoby Physics of plasmas 19 (3), 2012 | 26 | 2012 |
| Benchmarking offline reinforcement learning on real-robot hardware N Gürtler, S Blaes, P Kolev, F Widmaier, M Wüthrich, S Bauer, ... arXiv preprint arXiv:2307.15690, 2023 | 21 | 2023 |
| Versatile skill control via self-supervised adversarial imitation of unlabeled mixed motions C Li, S Blaes, P Kolev, M Vlastelica, J Frey, G Martius 2023 IEEE international conference on robotics and automation (ICRA), 2944-2950, 2023 | 17 | 2023 |
| Curious exploration via structured world models yields zero-shot object manipulation C Sancaktar, S Blaes, G Martius Advances in Neural Information Processing Systems 35, 24170-24183, 2022 | 14 | 2022 |
| Extracting strong policies for robotics tasks from zero-order trajectory optimizers C Pinneri, S Sawant, S Blaes, G Martius International Conference on Learning Representations, 2020 | 11 | 2020 |
| Risk-averse zero-order trajectory optimization M Vlastelica, S Blaes, C Pinneri, G Martius 5th Annual Conference on Robot Learning, 2021 | 9 | 2021 |
| Optimistic Active Exploration of Dynamical Systems B Sukhija, L Treven, C Sancaktar, S Blaes, S Coros, A Krause arXiv preprint arXiv:2306.12371, 2023 | 4 | 2023 |
| Real robot challenge 2022: Learning dexterous manipulation from offline data in the real world N Gürtler, F Widmaier, C Sancaktar, S Blaes, P Kolev, S Bauer, ... NeurIPS 2022 Competition Track, 133-150, 2023 | 2 | 2023 |
| Attentional bias through oscillatory coherence between excitatory activity and inhibitory minima S Blaes, T Burwick Neural Computation 27 (7), 1405-1437, 2015 | 2 | 2015 |
| Optimistic Active Exploration of Dynamical Systems L Treven, C Sancaktar, S Blaes, S Coros, A Krause Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Mind the Uncertainty: Risk-Aware and Actively Exploring Model-Based Reinforcement Learning M Vlastelica, S Blaes, C Pineri, G Martius arXiv preprint arXiv:2309.05582, 2023 | | 2023 |
| Nature-inspired inductive biases in learning robots S Blaes Universität Tübingen, 2022 | | 2022 |
| Feature binding in deep convolution networks with recurrences, oscillations, and top-down modulated dynamics. M Mundt, S Blaes, T Burwick ESANN, 2016 | | 2016 |
Georg MartiusUniversity of Tübingen and Max Planck Institute for Intelligent SystemsVerified email at uni-tuebingen.de
