Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models
暂无分享,去创建一个
[1] H. Akaike. A new look at the statistical model identification , 1974 .
[2] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[3] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[4] Russell L. Andersson,et al. Aggressive trajectory generator for a robot ping-pong player , 1988, IEEE Control Systems Magazine.
[5] Weiping Li,et al. Applied Nonlinear Control , 1991 .
[6] Ales Ude,et al. Trajectory generation from noisy positions of object features for teaching robot paths , 1993, Robotics Auton. Syst..
[7] David A. Cohn,et al. Active Learning with Statistical Models , 1996, NIPS.
[8] Stefan Schaal,et al. Is imitation learning the route to humanoid robots? , 1999, Trends in Cognitive Sciences.
[9] Stefan Schaal,et al. Locally Weighted Projection Regression : An O(n) Algorithm for Incremental Real Time Learning in High Dimensional Space , 2000 .
[10] Geoffrey J. McLachlan,et al. Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.
[11] Jun Nakanishi,et al. Movement imitation with nonlinear dynamical systems in humanoid robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).
[12] Bradley P. Carlin,et al. Bayesian measures of model complexity and fit , 2002 .
[13] K. Dautenhahn,et al. The agent-based perspective on imitation , 2002 .
[14] Neil D. Lawrence,et al. Fast Forward Selection to Speed Up Sparse Gaussian Process Regression , 2003, AISTATS.
[15] Dong-Soo Kwon,et al. Mobile robots at your fingertip: Bezier curve on-line trajectory generation for supervisory control , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).
[16] Stefan Schaal,et al. Computational approaches to motor learning by imitation. , 2003, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[17] Katsu Yamane,et al. Synthesizing animations of human manipulation tasks , 2004, ACM Trans. Graph..
[18] S. Bocionek,et al. Robot programming by Demonstration (RPD): Supporting the induction by human interaction , 1996, Machine Learning.
[19] Stefan Schaal,et al. Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning , 2002, Applied Intelligence.
[20] Gordon Cheng,et al. Discovering optimal imitation strategies , 2004, Robotics Auton. Syst..
[21] Zoubin Ghahramani,et al. Sparse Gaussian Processes using Pseudo-inputs , 2005, NIPS.
[22] Stefano Caselli,et al. Robust trajectory learning and approximation for robot programming by demonstration , 2006, Robotics Auton. Syst..
[23] Katta G. Murty,et al. Nonlinear Programming Theory and Algorithms , 2007, Technometrics.
[24] Aude Billard,et al. On Learning, Representing, and Generalizing a Task in a Humanoid Robot , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[25] Duy Nguyen-Tuong,et al. Local Gaussian Process Regression for Real Time Online Model Learning , 2008, NIPS.
[26] Dana Kulic,et al. Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains , 2008, Int. J. Robotics Res..
[27] Eduardo Sontag. Input to State Stability: Basic Concepts and Results , 2008 .
[28] P. Deb. Finite Mixture Models , 2008 .
[29] Aude Billard,et al. Dynamical System Modulation for Robot Learning via Kinesthetic Demonstrations , 2008, IEEE Transactions on Robotics.
[30] Stefan Schaal,et al. Robot Programming by Demonstration , 2009, Springer Handbook of Robotics.
[31] Pieter Abbeel,et al. Learning for control from multiple demonstrations , 2008, ICML '08.
[32] Jochen J. Steil,et al. Task-level imitation learning using variance-based movement optimization , 2009, 2009 IEEE International Conference on Robotics and Automation.
[33] Stefan Schaal,et al. Learning and generalization of motor skills by learning from demonstration , 2009, 2009 IEEE International Conference on Robotics and Automation.
[34] Henk Nijmeijer,et al. Robot Programming by Demonstration , 2010, SIMPAR.
[35] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[36] A. Billard,et al. Learning the Nonlinear Multivariate Dynamics of Motion of Robotic Manipulators , 2009 .
[37] Aude Billard,et al. Learning nonlinear multi-variate motion dynamics for real-time position and orientation control of robotic manipulators , 2009, 2009 9th IEEE-RAS International Conference on Humanoid Robots.
[38] Aude Billard,et al. Imitation learning of globally stable non-linear point-to-point robot motions using nonlinear programming , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[39] Aude Billard,et al. BM: An iterative algorithm to learn stable non-linear dynamical systems with Gaussian mixture models , 2010, 2010 IEEE International Conference on Robotics and Automation.
[40] Eric L. Sauser,et al. Tactile guidance for policy refinement and reuse , 2010, 2010 IEEE 9th International Conference on Development and Learning.
[41] Darwin G. Caldwell,et al. Learning and Reproduction of Gestures by Imitation , 2010, IEEE Robotics & Automation Magazine.
[42] Eric L. Sauser,et al. An Approach Based on Hidden Markov Model and Gaussian Mixture Regression , 2010 .