| Reverse curriculum generation for reinforcement learning C Florensa, D Held, M Wulfmeier, M Zhang, P Abbeel Conference on robot learning, 482-495, 2017 | 531 | 2017 |
| Maximum entropy deep inverse reinforcement learning M Wulfmeier, P Ondruska, I Posner arXiv preprint arXiv:1507.04888, 2015 | 507 | 2015 |
| Large-scale cost function learning for path planning using deep inverse reinforcement learning M Wulfmeier, D Rao, DZ Wang, P Ondruska, I Posner The International Journal of Robotics Research 36 (10), 1073-1087, 2017 | 205 | 2017 |
| Incremental Adversarial Domain Adaptation for Continually Changing Environments M Wulfmeier, A Bewley, I Posner arXiv preprint arXiv:1712.07436, 2017 | 150 | 2017 |
| Watch this: Scalable cost-function learning for path planning in urban environments M Wulfmeier, DZ Wang, I Posner 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2016 | 145 | 2016 |
| From motor control to team play in simulated humanoid football S Liu, G Lever, Z Wang, J Merel, SMA Eslami, D Hennes, WM Czarnecki, ... Science Robotics 7 (69), eabo0235, 2022 | 119 | 2022 |
| Taco: Learning task decomposition via temporal alignment for control K Shiarlis, M Wulfmeier, S Salter, S Whiteson, I Posner International Conference on Machine Learning, 4654-4663, 2018 | 106 | 2018 |
| Continuous-discrete reinforcement learning for hybrid control in robotics M Neunert, A Abdolmaleki, M Wulfmeier, T Lampe, T Springenberg, ... Conference on Robot Learning, 735-751, 2020 | 104 | 2020 |
| Learning agile soccer skills for a bipedal robot with deep reinforcement learning T Haarnoja, B Moran, G Lever, SH Huang, D Tirumala, J Humplik, ... Science Robotics 9 (89), eadi8022, 2024 | 101 | 2024 |
| Addressing appearance change in outdoor robotics with adversarial domain adaptation M Wulfmeier, A Bewley, I Posner 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017 | 93* | 2017 |
| Mutual alignment transfer learning M Wulfmeier, I Posner, P Abbeel Conference on Robot Learning, 281-290, 2017 | 91 | 2017 |
| Deep inverse reinforcement learning M Wulfmeier, P Ondruska, I Posner CoRR, abs/1507.04888, 2015 | 83 | 2015 |
| Design and implementation of a particle image velocimetry method for analysis of running gear–soil interaction C Senatore, M Wulfmeier, I Vlahinić, J Andrade, K Iagnemma Journal of Terramechanics 50 (5-6), 311-326, 2013 | 59 | 2013 |
| Towards a unified agent with foundation models N Di Palo, A Byravan, L Hasenclever, M Wulfmeier, N Heess, ... arXiv preprint arXiv:2307.09668, 2023 | 52 | 2023 |
| Data-efficient hindsight off-policy option learning M Wulfmeier, D Rao, R Hafner, T Lampe, A Abdolmaleki, T Hertweck, ... International Conference on Machine Learning, 11340-11350, 2021 | 51 | 2021 |
| Imitate and repurpose: Learning reusable robot movement skills from human and animal behaviors S Bohez, S Tunyasuvunakool, P Brakel, F Sadeghi, L Hasenclever, ... arXiv preprint arXiv:2203.17138, 2022 | 47 | 2022 |
| Is bang-bang control all you need? solving continuous control with bernoulli policies T Seyde, I Gilitschenski, W Schwarting, B Stellato, M Riedmiller, ... Advances in Neural Information Processing Systems 34, 27209-27221, 2021 | 45 | 2021 |
| The challenges of exploration for offline reinforcement learning N Lambert, M Wulfmeier, W Whitney, A Byravan, M Bloesch, V Dasagi, ... arXiv preprint arXiv:2201.11861, 2022 | 40 | 2022 |
| Compositional transfer in hierarchical reinforcement learning M Wulfmeier, A Abdolmaleki, R Hafner, JT Springenberg, M Neunert, ... arXiv preprint arXiv:1906.11228, 2019 | 38 | 2019 |
| Towards general and autonomous learning of core skills: A case study in locomotion R Hafner, T Hertweck, P Klöppner, M Bloesch, M Neunert, M Wulfmeier, ... Conference on Robot Learning, 1084-1099, 2021 | 33 | 2021 |
Ingmar PosnerOxford UniversityEmail verificata su robots.ox.ac.uk
