Books, Book Chapters & Theses
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    Deisenroth M. P.; Szepesvari C.; Peters J. (2012)., in: Deisenroth M. P.; Szepesvari C., Peters J. (eds.), Proceedings of the 10th European Workshop on Reinforcement Learning, 24.
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    Deisenroth, M.P. (2010). Efficient Reinforcement Learning Using Gaussian Processes, in: Uwe D. Hanebeck (eds.), 9, KIT Scientific Publishing.
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    Deisenroth, M.P. (2009). Efficient Reinforcement Learning Using Gaussian Processes.
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    Rasmussen, C.E.; Deisenroth, M.P. (2008). Probabilistic Inference for Fast Learning in Control, Recent Advances in Reinforcement Learning (LNCS).
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    Deisenroth, M.P. (2006). An Online Computation Approach to Optimal Finite-Horizon State-Feedback Control of Nonlinear Stochastic Systems.
 
Journal Papers
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    Kupcsik, A.G.; Deisenroth, M.P.; Peters, J.; Ai Poh, L.; Vadakkepat, V.; Neumann, G. (2017). Model-based Contextual Policy Search for Data-Efficient Generalization of Robot Skills, Artificial Intelligence, 247, pp.415-439.
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    Calandra, R.; Seyfarth, A.; Peters, J.; Deisenroth, M. (2015). Bayesian Optimization for Learning Gaits under Uncertainty, Annals of Mathematics and Artificial Intelligence (AMAI).
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    Deisenroth, M.P.; Fox, D.; Rasmussen, C.E. (2014). Gaussian Processes for Data-Efficient Learning in Robotics and Control, IEEE Transactions on Pattern Analysis and Machine Intelligence.
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    Wang, Z.; Muelling, K.; Deisenroth, M. P.; Ben Amor, H.; Vogt, D.; Schoelkopf, B.; Peters, J. (2013). Probabilistic Movement Modeling for Intention Inference in Human-Robot Interaction, International Journal of Robotics Research (IJRR), 32, 7, pp.841-858.
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    Englert, P.; Paraschos, A.; Peters, J.;Deisenroth, M.P. (2013). Probabilistic Model-based Imitation Learning, Adaptive Behavior Journal, 21, pp.388-403.
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    Deisenroth, M. P.; Neumann, G.; Peters, J. (2013). A Survey on Policy Search for Robotics, Foundations and Trends in Robotics, 21, pp.388-403.
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    Deisenroth, M.P.; Turner, R.; Huber, M.; Hanebeck, U.D.; Rasmussen, C.E (2012). Robust Filtering and Smoothing with Gaussian Processes, IEEE Transactions on Automatic Control.
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    Deisenroth, M.P.; Rasmussen, C.E.; Peters, J. (2009). Gaussian Process Dynamic Programming, Neurocomputing, 72, pp.1508-1524.
 
Conference and Workshop Papers
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    Calandra, R.; Peters, J.; Rasmussen, C.E.; Deisenroth, M.P. (2016). Manifold Gaussian Processes for Regression, Proceedings of the International Joint Conference on Neural Networks (IJCNN).
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    Calandra, R.; Ivaldi, S.; Deisenroth, M.;Rueckert, E.; Peters, J. (2015). Learning Inverse Dynamics Models with Contacts, Proceedings of the International Conference on Robotics and Automation (ICRA).
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    Calandra, R.; Ivaldi, S.; Deisenroth, M.; Peters, J. (2015). Learning Torque Control in Presence of Contacts using Tactile Sensing from Robot Skin, Proceedings of the International Conference on Humanoid Robots (HUMANOIDS).
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    Deisenroth, M.P.; Englert, P.; Peters, J.; Fox, D. (2014). Multi-Task Policy Search for Robotics, Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
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    Bischoff, B.; Nguyen-Tuong, D.; van Hoof, H. McHutchon, A.; Rasmussen, C.E.; Knoll, A.; Peters, J.; Deisenroth, M.P. (2014). Policy Search For Learning Robot Control Using Sparse Data, Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
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    Calandra, R.; Seyfarth, A.; Peters, J.; Deisenroth, M.P. (2014). An Experimental Comparison of Bayesian Optimization for Bipedal Locomotion, Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA).
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    Haji Ghassemi, N.; Deisenroth, M.P. (2014). Approximate Inference for Long-Term Forecasting with Periodic Gaussian Processes, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).
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    Calandra, R.; Gopalan, N.; Seyfarth, A.; Peters, J.; Deisenroth, M.P. (2014). Bayesian Gait Optimization for Bipedal Locomotion, Proceedings of the 2014 Learning and Intelligent Optimization Conference (LION8).
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    Kamthe, S.; Peters, J.; Deisenroth, M. (2014). Multi-modal filtering for non-linear estimation, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP).
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    Englert, P.; Paraschos, A.; Peters, J.; Deisenroth, M. P. (2013). Model-based Imitation Learning by Probabilistic Trajectory Matching, Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA).
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    Gopalan, N.; Deisenroth, M. P.; Peters, J. (2013). Feedback Error Learning for Rhythmic Motor Primitives, Proceedings of 2013 IEEE International Conference on Robotics and Automation (ICRA).
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    Kupcsik, A.G.; Deisenroth, M.P.; Peters, J.; Neumann, G. (2013). Data-Efficient Generalization of Robot Skills with Contextual Policy Search, Proceedings of the National Conference on Artificial Intelligence (AAAI) .
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    Neumann, G.; Kupcsik, A.G.; Deisenroth, M.P.; Peters, J. (2013). Information-Theoretic Motor Skill Learning, Proceedings of the AAAI 2013 Workshop on Intelligent Robotic Systems.
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    Wang, Z.;Deisenroth, M; Ben Amor, H.; Vogt, D.; Schoelkopf, B.; Peters, J. (2012). Probabilistic Modeling of Human Movements for Intention Inference, Proceedings of Robotics: Science and Systems (R:SS).
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    Deisenroth, M.P.; Mohamed, S. (2012). Expectation Propagation in Gaussian Process Dynamical Systems, Advances in Neural Information Processing Systems 26 (NIPS/NeurIPS), Cambridge, MA: MIT Press., The MIT Press.
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    Deisenroth, M.P.; Calandra, R.; Seyfarth, A.; Peters, J. (2012). Toward Fast Policy Search for Learning Legged Locomotion, Proceedings of the International Conference on Robot Systems (IROS).
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    Deisenroth, M.P.; Peters, J. (2012). Solving Nonlinear Continuous State-Action-Observation POMDPs for Mechanical Systems with Gaussian Noise, Proceedings of the European Workshop on Reinforcement Learning (EWRL).
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    Calandra, R.; Raiko, T.; Deisenroth, M.P.; Montesino Pouzols, F. (2012). Learning Deep Belief Networks from Non-Stationary Streams, International Conference on Artificial Neural Networks (ICANN).
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    Deisenroth, M.P.; Rasmussen, C.E. (2011). PILCO: A Model-Based and Data-Efficient Approach to Policy Search, International Conference on Machine Learning (ICML 2011).
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    Deisenroth, M.P.; Fox, D.; Rasmussen, C.E. (2011). Learning to Control a Low-Cost Robotic Manipulator Using Data-Efficient Reinforcement Learning, Robotics: Science & Systems (RSS 2011).
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    Deisenroth, M.P.; Ohlsson, H. (2011). A General Perspective on Gaussian Filtering and Smoothing: Explaining Current and Deriving new Algorithms, American Control Conference (ACC 2011).
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    Cynthia Matuszek, Brian Mayton, Robert Aimi, Marc P. Deisenroth, Liefeng Bo, Robert Chu, Mike Kung, Louis LeGrand, Joshua R. Smith and Dieter Fox (2011). Gambit: An Autonomous Chess-Playing Robotic System, International Conference on Robotics and Automation (ICRA 2011).
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    Turner, R.; Deisenroth, M.P., Rasmussen, C.E. (2010). State-Space Inference and Learning with Gaussian Processes , International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, vol. JMLR: W&C 9.
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    Deisenroth, M.P.; Huber, M.; Hanebeck, U.D. (2009). Analytic Moment-based Gaussian Process Filtering, International Conference on Machine Learning (ICML 2009).
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    Deisenroth, M.P.; Peters, J.; Rasmussen, C.E. (2008). Approximate Dynamic Programming with Gaussian Processes, American Control Conference.
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    Deisenroth, M.P.; Rasmussen, C.E.; Peters, J. (2008). Model-Based Reinforcement Learning with Continuous States and Actions, Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2008), pp.19-24.
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    Deisenroth, M.P.; Weissel, F.; Ohtsuka, T.; Hanebeck, U.D. (2007). Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces, European Control Conference (ECC).
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    Deisenroth, M.P.; Weissel, F.; Ohtsuka, T.; Brunn, D.; Hanebeck, U.D. (2006). Finite-Horizon Optimal State-Feedback Control of Nonlinear Stochastic Systems Based on a Minimum Principle , Proceedings of the 6th IEEE International Conference on Multisensor Fusion and Integration (MFI 2006), pp.371-376.
 
Theses
  •     Bib
    Deisenroth, M.P. (2009). Efficient Reinforcement Learning Using Gaussian Processes.
  •   Bib
    Deisenroth, M.P. (2006). An Online Computation Approach to Optimal Finite-Horizon State-Feedback Control of Nonlinear Stochastic Systems.