Data-Efficient Model Learning and Prediction for Contact-Rich Manipulation Tasks
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] Gérard Bloch,et al. Piecewise smooth system identification in reproducing kernel Hilbert space , 2014, 53rd IEEE Conference on Decision and Control.
[2] Ross A. Knepper,et al. DeepMPC: Learning Deep Latent Features for Model Predictive Control , 2015, Robotics: Science and Systems.
[3] Sergey Levine,et al. Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models , 2018, NeurIPS.
[4] 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).
[5] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[6] Jan Peters,et al. A Survey on Policy Search for Robotics , 2013, Found. Trends Robotics.
[7] Nolan Wagener,et al. Learning contact-rich manipulation skills with guided policy search , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[8] Carl E. Rasmussen,et al. Gaussian Processes for Data-Efficient Learning in Robotics and Control , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[9] Jan Bender,et al. Constraint-based collision and contact handling using impulses , 2006 .
[10] Siddhartha S. Srinivasa,et al. Unsupervised Learning for Nonlinear PieceWise Smooth Hybrid Systems , 2017, ArXiv.
[11] Michael I. Jordan,et al. Variational inference for Dirichlet process mixtures , 2006 .
[12] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[13] Uwe D. Hanebeck,et al. Analytic moment-based Gaussian process filtering , 2009, ICML '09.
[14] Ana Paiva,et al. An ensemble inverse optimal control approach for robotic task learning and adaptation , 2019, Auton. Robots.
[15] Jeffrey K. Uhlmann,et al. New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.
[16] Jan Peters,et al. Learning inverse dynamics models with contacts , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[17] Jan Lunze,et al. Handbook of hybrid systems control : theory, tools, applications , 2009 .
[18] Carl E. Rasmussen,et al. Manifold Gaussian Processes for regression , 2014, 2016 International Joint Conference on Neural Networks (IJCNN).
[19] M. Escobar,et al. Bayesian Density Estimation and Inference Using Mixtures , 1995 .
[20] 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).
[21] Brian Charles Williams,et al. Learning Hybrid Models with Guarded Transitions , 2015, AAAI.
[22] René Vidal,et al. Identification of Hybrid Systems: A Tutorial , 2007, Eur. J. Control.
[23] Russ Tedrake,et al. Erratum: Direct Trajectory Optimization of Rigid Body Dynamical Systems through Contact , 2012, WAFR.
[24] Scott W. Linderman,et al. Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems , 2017, AISTATS.
[25] Mübeccel Demirekler,et al. Analysis of single Gaussian approximation of Gaussian mixtures in Bayesian filtering applied to mixed multiple-model estimation , 2007, Int. J. Control.
[26] Geoffrey J. Gordon,et al. A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning , 2010, AISTATS.
[27] Jan Peters,et al. Model learning for robot control: a survey , 2011, Cognitive Processing.
[28] Russ Tedrake,et al. Direct Trajectory Optimization of Rigid Body Dynamical Systems through Contact , 2012, WAFR.
[29] 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.