Markus Wulfmeier
Cited by
Cited by
Reverse curriculum generation for reinforcement learning
C Florensa, D Held, M Wulfmeier, M Zhang, P Abbeel
Conference on robot learning, 482-495, 2017
Maximum entropy deep inverse reinforcement learning
M Wulfmeier, P Ondruska, I Posner
arXiv preprint arXiv:1507.04888, 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
Incremental Adversarial Domain Adaptation for Continually Changing Environments
M Wulfmeier, A Bewley, I Posner
arXiv preprint arXiv:1712.07436, 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 Systemsá…, 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
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
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
Mutual alignment transfer learning
M Wulfmeier, I Posner, P Abbeel
Conference on Robot Learning, 281-290, 2017
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 Systemsá…, 2017
Deep inverse reinforcement learning
M Wulfmeier, P Ondruska, I Posner
CoRR, abs/1507.04888, 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
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
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
Towards a unified agent with foundation models
N Di Palo, A Byravan, L Hasenclever, M Wulfmeier, N Heess, ...
arXiv preprint arXiv:2307.09668, 2023
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
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
Compositional transfer in hierarchical reinforcement learning
M Wulfmeier, A Abdolmaleki, R Hafner, JT Springenberg, M Neunert, ...
arXiv preprint arXiv:1906.11228, 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
Towards general and autonomous learning of core skills: A case study in locomotion
R Hafner, T Hertweck, P Kl÷ppner, M Bloesch, M Neunert, M Wulfmeier, ...
Conference on Robot Learning, 1084-1099, 2021
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