暂无分享,去创建一个
Danica Kragic | Hang Yin | Pietro Falco | Shahbaz Abdul Khader | D. Kragic | Hang Yin | P. Falco | S. A. Khader
[1] Jan Peters,et al. A Survey on Policy Search for Robotics , 2013, Found. Trends Robotics.
[2] Sergey Levine,et al. Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[3] Dieter Fox,et al. GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[4] Jeffrey K. Uhlmann,et al. New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.
[5] Duy Nguyen-Tuong,et al. Local Gaussian Process Regression for Real Time Online Model Learning , 2008, NIPS.
[6] M. Escobar,et al. Bayesian Density Estimation and Inference Using Mixtures , 1995 .
[7] Michael I. Jordan,et al. Variational inference for Dirichlet process mixtures , 2006 .
[8] Jan Peters,et al. Model learning for robot control: a survey , 2011, Cognitive Processing.
[9] Ross A. Knepper,et al. DeepMPC: Learning Deep Latent Features for Model Predictive Control , 2015, Robotics: Science and Systems.
[10] Scott W. Linderman,et al. Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems , 2017, AISTATS.
[11] Carl E. Rasmussen,et al. Gaussian Processes for Data-Efficient Learning in Robotics and Control , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[12] Yoshua Bengio,et al. An Input Output HMM Architecture , 1994, NIPS.
[13] Siddhartha S. Srinivasa,et al. Unsupervised Learning for Nonlinear PieceWise Smooth Hybrid Systems , 2017, ArXiv.
[14] Uwe D. Hanebeck,et al. Analytic moment-based Gaussian process filtering , 2009, ICML '09.
[15] Carl E. Rasmussen,et al. Infinite Mixtures of Gaussian Process Experts , 2001, NIPS.
[16] Jan Peters,et al. Learning inverse dynamics models with contacts , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[17] Jan Peters,et al. Reinforcement learning in robotics: A survey , 2013, Int. J. Robotics Res..
[18] Jan Lunze,et al. Handbook of hybrid systems control : theory, tools, applications , 2009 .
[19] Nolan Wagener,et al. Learning contact-rich manipulation skills with guided policy search , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[20] Carl E. Rasmussen,et al. The Infinite Gaussian Mixture Model , 1999, NIPS.
[21] Carl E. Rasmussen,et al. Manifold Gaussian Processes for regression , 2014, 2016 International Joint Conference on Neural Networks (IJCNN).
[22] Alexis Boukouvalas,et al. GPflow: A Gaussian Process Library using TensorFlow , 2016, J. Mach. Learn. Res..
[23] Geoffrey J. Gordon,et al. A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning , 2010, AISTATS.
[24] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[25] Marc Toussaint,et al. Learning discontinuities with products-of-sigmoids for switching between local models , 2005, ICML.
[26] Sergey Levine,et al. Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models , 2018, NeurIPS.
[27] Christopher D. McKinnon,et al. Learning multimodal models for robot dynamics online with a mixture of Gaussian process experts , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[28] Ana Paiva,et al. An ensemble inverse optimal control approach for robotic task learning and adaptation , 2019, Auton. Robots.
[29] René Vidal,et al. Identification of Hybrid Systems: A Tutorial , 2007, Eur. J. Control.
[30] Jean-Baptiste Mouret,et al. Black-box data-efficient policy search for robotics , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[31] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[32] Brian Charles Williams,et al. Learning Hybrid Models with Guarded Transitions , 2015, AAAI.