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This paper presents a problem of model learning for the purpose of learning how to navigate a ball to a goal state in a circular maze environment with two degrees of freedom.
MIT press Cambridge, 1998
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D. Nguyen-Tuong and J. Peters, “Using model knowledge for learning inverse dynamics,” in IEEE International Conference on Robotics and Automation
2010
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Springer Science & Business Media, 2010
B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo, Robotics: modelling, planning and control · 2010
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M. Deisenroth and C. E. Rasmussen, “PILCO: A model-based and data-efficient approach to policy search,” in Proceedings of the 28th International Conference on machine learning (ICML-11)
2011
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A. Ranganathan, M. H. Yang, and J. Ho, “Online sparse Gaussian process regression and its applications,” IEEE Transactions on Image Processing
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A. Gijsberts and G. Metta, “Incremental learning of robot dynamics using random features,” in IEEE International Conference on Robotics and Automation (ICRA)
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R. Lioutikov, G. Neumann, G. Maeda, and J. Peters, “Probabilistic segmentation applied to an assembly task,” in Humanoid Robots (Humanoids), 2015 IEEE-RAS 15th International Conference on
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O. Kroemer, C. Daniel, G. Neumann, H. Van Hoof, and J. Peters, “Towards learning hierarchical skills for multi-phase manipulation tasks,” in Robotics and Automation (ICRA), 2015 IEEE International Conference on
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M. Zucker and J. A. Bagnell, “Reinforcement planning: RL for optimal planners,” in Robotics and Automation (ICRA), 2012 IEEE International Conference on
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T. Wu and J. Movellan, “Semi-parametric Gaussian process for robot system identification,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Y. Tassa, T. Erez, and E. Todorov, “Synthesis and stabilization of complex behaviors through online trajectory optimization,” in Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on
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S. Levine and V. Koltun, “Guided policy search,” in Proceedings of the 30th International Conference on Machine Learning (ICML-13)
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J. Boedecker, J. T. Springenberg, J. Wülfing, and M. Riedmiller, “Approximate real-time optimal control based on sparse gaussian process models,” in Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2014 IEEE Symposium on
2014
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T. Anjali and S. S. Mathew, “Implementation of optimal control for ball and beam system,” in Emerging Technological Trends (ICETT), International Conference on
2016
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D. Romeres, G. Prando, G. Pillonetto, and A. Chiuso, “On-line bayesian system identification,” in Control Conference (ECC), 2016 European
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2017
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N. Fazeli, S. Zapolsky, E. Drumwright, and A. Rodriguez, “Learning data-efficient rigid-body contact models: Case study of planar impact,” in Conference on Robot Learning
2017
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2017
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2018
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J. van Baar, A. Sullivan, R. Cordorel, D. Jha, D. Romeres, and D. Nikovski, “Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics,” ArXiv e-prints
2018
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D. Romeres, M. Zorzi, R. Camoriano, S. Traversaro, and A. Chiuso, “Derivative-free online learning of inverse dynamics models,” ArXiv e-prints
2018
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