| Reverse curriculum generation for reinforcement learning C Florensa, D Held, M Wulfmeier, M Zhang, P Abbeel Conference on robot learning, 482-495, 2017 | 439 | 2017 |
| Maximum entropy deep inverse reinforcement learning M Wulfmeier, P Ondruska, I Posner arXiv preprint arXiv:1507.04888, 2015 | 396 | 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 | 171 | 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 | 130 | 2016 |
| Incremental Adversarial Domain Adaptation for Continually Changing Environments M Wulfmeier, A Bewley, I Posner arXiv preprint arXiv:1712.07436, 2017 | 127 | 2017 |
| 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 | 88 | 2018 |
| 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 | 86 | 2022 |
| 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 | 86 | 2017 |
| Mutual alignment transfer learning M Wulfmeier, I Posner, P Abbeel Conference on Robot Learning, 281-290, 2017 | 80 | 2017 |
| 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 | 77 | 2020 |
| Deep inverse reinforcement learning M Wulfmeier, P Ondruska, I Posner CoRR, abs/1507.04888, 2015 | 76 | 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 | 57 | 2013 |
| 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 | 36 | 2021 |
| 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 | 30 | 2021 |
| Compositional transfer in hierarchical reinforcement learning M Wulfmeier, A Abdolmaleki, R Hafner, JT Springenberg, M Neunert, ... arXiv preprint arXiv:1906.11228, 2019 | 30 | 2019 |
| Investigation of stress and failure in granular soils for lightweight robotic vehicle applications C Senatore, M Wulfmeier, P Jayakumar, J Maclennan, K Iagnemma the 2012 Ground Vehicle Systems Engineering and Technology Symposium (GVSETS …, 2012 | 27 | 2012 |
| 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 | 26 | 2022 |
| Regularized hierarchical policies for compositional transfer in robotics M Wulfmeier, A Abdolmaleki, R Hafner, JT Springenberg, M Neunert, ... | 26 | 2019 |
| 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 | 23 | 2021 |
| Is curiosity all you need? on the utility of emergent behaviours from curious exploration O Groth, M Wulfmeier, G Vezzani, V Dasagi, T Hertweck, R Hafner, ... arXiv preprint arXiv:2109.08603, 2021 | 21 | 2021 |
Ingmar PosnerOxford UniversityVerified email at robots.ox.ac.uk
