An introduction to variational methods for graphical models
Learning in graphical models
Variational methods for the Dirichlet process
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Learning and generalization of motor skills by learning from demonstration
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Programming-by-Demonstration of reaching motions-A next-state-planner approach
Robotics and Autonomous Systems
Task-specific generalization of discrete and periodic dynamic movement primitives
IEEE Transactions on Robotics
Signal Modeling and Classification Using a Robust Latent Space Model Based on Distributions
IEEE Transactions on Signal Processing
Music Analysis Using Hidden Markov Mixture Models
IEEE Transactions on Signal Processing
Visual learning by imitation with motor representations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Developmental Roadmap for Learning by Imitation in Robots
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Trajectory generation and modulation using dynamic neural networks
IEEE Transactions on Neural Networks
Gradient calculations for dynamic recurrent neural networks: a survey
IEEE Transactions on Neural Networks
Autonomous tactile perception: A combined improved sensing and Bayesian nonparametric approach
Robotics and Autonomous Systems
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In the past years, many authors have considered application of machine learning methodologies to effect robot learning by demonstration. Gaussian mixture regression (GMR) is one of the most successful methodologies used for this purpose. A major limitation of GMR models concerns automatic selection of the proper number of model states, i.e., the number of model component densities. Existing methods, including likelihood- or entropy-based criteria, usually tend to yield noisy model size estimates while imposing heavy computational requirements. Recently, Dirichlet process (infinite) mixture models have emerged in the cornerstone of nonparametric Bayesian statistics as promising candidates for clustering applications where the number of clusters is unknown a priori. Under this motivation, to resolve the aforementioned issues of GMR-based methods for robot learning by demonstration, in this paper we introduce a nonparametric Bayesian formulation for the GMR model, the Dirichlet process GMR model. We derive an efficient variational Bayesian inference algorithm for the proposed model, and we experimentally investigate its efficacy as a robot learning by demonstration methodology, considering a number of demanding robot learning by demonstration scenarios.