Markus Wulfmeier
Markus Wulfmeier
DeepMind
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Reverse curriculum generation for reinforcement learning
C Florensa, D Held, M Wulfmeier, M Zhang, P Abbeel
Conference on robot learning, 482-495, 2017
2432017
Maximum entropy deep inverse reinforcement learning
M Wulfmeier, P Ondruska, I Posner
arXiv preprint arXiv:1507.04888, 2015
2422015
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
872017
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
812016
Incremental Adversarial Domain Adaptation for Continually Changing Environments
M Wulfmeier, A Bewley, I Posner
arXiv preprint arXiv:1712.07436, 2017
612017
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
582017
Deep inverse reinforcement learning
M Wulfmeier, P Ondruska, I Posner
CoRR, abs/1507.04888, 2015
572015
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
472013
Mutual alignment transfer learning
M Wulfmeier, I Posner, P Abbeel
Conference on Robot Learning, 281-290, 2017
432017
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
382018
Compositional Transfer in Hierarchical Reinforcement Learning
M Wulfmeier, A Abdolmaleki, R Hafner, JT Springenberg, M Neunert, ...
25*2019
Investigation of stress and failure in granular soils for lightweight robotic vehicle applications
C Senatore, M Wulfmeier, J MacLennan, P Jayakumar, K Iagnemma
ARMY TANK AUTOMOTIVE RESEARCH DEVELOPMENT AND ENGINEERING CENTER WARREN MI, 2012
242012
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
142020
Incorporating human domain knowledge into large scale cost function learning
M Wulfmeier, D Rao, I Posner
arXiv preprint arXiv:1612.04318, 2016
132016
Voronoi-based heuristic for nonholonomic search-based path planning
Q Wang, M Wulfmeier, B Wagner
Intelligent Autonomous Systems 13, 445-458, 2016
122016
Disentangled cumulants help successor representations transfer to new tasks
C Grimm, I Higgins, A Barreto, D Teplyashin, M Wulfmeier, T Hertweck, ...
arXiv preprint arXiv:1911.10866, 2019
82019
On machine learning and structure for mobile robots
M Wulfmeier
arXiv preprint arXiv:1806.06003, 2018
82018
Neural Stethoscopes: Unifying analytic, auxiliary and adversarial network probing
FB Fuchs, O Groth, AR Kosiorek, A Bewley, M Wulfmeier, A Vedaldi, ...
CoRR abs/1806.05502, 2018
7*2018
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, ...
arXiv preprint arXiv:2008.12228, 2020
52020
Attention-privileged reinforcement learning
S Salter, D Rao, M Wulfmeier, R Hadsell, I Posner
arXiv preprint arXiv:1911.08363, 2019
5*2019
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