| Meta learning via learned loss S Bechtle, A Molchanov, Y Chebotar, E Grefenstette, L Righetti, ... 2020 25th International Conference on Pattern Recognition (ICPR), 4161-4168, 2021 | 109 | 2021 |
| Model-based inverse reinforcement learning from visual demonstrations N Das, S Bechtle, T Davchev, D Jayaraman, A Rai, F Meier Conference on Robot Learning, 1930-1942, 2021 | 66 | 2021 |
| Curious ilqr: Resolving uncertainty in model-based rl S Bechtle, Y Lin, A Rai, L Righetti, F Meier Conference on Robot Learning, 162-171, 2020 | 43 | 2020 |
| On the sense of agency and of object permanence in robots S Bechtle, G Schillaci, VV Hafner 2016 Joint IEEE International Conference on Development and Learning and …, 2016 | 14 | 2016 |
| A generalist dynamics model for control I Schubert, J Zhang, J Bruce, S Bechtle, E Parisotto, M Riedmiller, ... arXiv preprint arXiv:2305.10912, 2023 | 12 | 2023 |
| Genie: Generative Interactive Environments J Bruce, M Dennis, A Edwards, J Parker-Holder, Y Shi, E Hughes, M Lai, ... arXiv preprint arXiv:2402.15391, 2024 | 7 | 2024 |
| Learning time-invariant reward functions through model-based inverse reinforcement learning T Davchev, S Bechtle, S Ramamoorthy, F Meier arXiv preprint arXiv:2107.03186, 2021 | 5 | 2021 |
| Learning extended body schemas from visual keypoints for object manipulation S Bechtle, N Das, F Meier arXiv preprint arXiv:2011.03882, 2020 | 4 | 2020 |
| Leveraging forward model prediction error for learning control S Bechtle, B Hammoud, A Rai, F Meier, L Righetti 2021 IEEE International Conference on Robotics and Automation (ICRA), 4445-4451, 2021 | 3 | 2021 |
| First steps towards the development of the sense of object permanence in robots S Bechtle, G Schillaci, VV Hafner 2015 Joint IEEE International Conference on Development and Learning and …, 2015 | 3 | 2015 |
| Offline Actor-Critic Reinforcement Learning Scales to Large Models JT Springenberg, A Abdolmaleki, J Zhang, O Groth, M Bloesch, T Lampe, ... arXiv preprint arXiv:2402.05546, 2024 | 1 | 2024 |
| Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots T Lampe, A Abdolmaleki, S Bechtle, SH Huang, JT Springenberg, ... arXiv preprint arXiv:2312.11374, 2023 | 1 | 2023 |
| Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities M Wulfmeier, A Byravan, S Bechtle, K Hausman, N Heess arXiv preprint arXiv:2312.01939, 2023 | 1 | 2023 |
| Equivariant data augmentation for generalization in offline reinforcement learning C Pinneri, S Bechtle, M Wulfmeier, A Byravan, J Zhang, WF Whitney, ... arXiv preprint arXiv:2309.07578, 2023 | 1 | 2023 |
| Multimodal learning of keypoint predictive models for visual object manipulation S Bechtle, N Das, F Meier IEEE Transactions on Robotics 39 (2), 1212-1224, 2023 | 1 | 2023 |
| Towards a humanoid-oriented movement writing A Stoica, HJ Suh, SM Hewitt, S Bechtle, A Gruebler, Y Iwashita 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2017 | 1 | 2017 |
| Lifelong learning in the real world SME Bechtle Universität Tübingen, 2022 | | 2022 |
| Model Based Meta Learning of Critics for Policy Gradients S Bechtle, L Righetti, F Meier arXiv preprint arXiv:2204.02210, 2022 | | 2022 |
| Exploring by Exploiting Bad Models in Model-Based Reinforcement Learning Y Lin, S Bechtle, L Righetti, A Rai, F Meier | | 2019 |
Franziska MeierResearch Scientist, Facebook AI ResearchE-mail confirmado em fb.com
