Gaussian Mixture Models have been widely used in robotic control and in sensory anticipation applications. A mixture model is learnt from demonstrations and later used to infer the most likely control signals, or is also used as a forward model to predict the change in sensory signals over time. However, such models often are too big to be tractable in real-time applications. In this paper we introduce the Context-GMM, a method to learn sparse priors over the mixture components. Such priors are stable over large amounts of time and provide a way of selecting very small subsets of mixture components without significant loss in accuracy and with huge computational savings. © 2012 IEEE.
CITATION STYLE
Ribes, A., Bueno, J. C., Demiris, Y., & De Mantaras, R. L. (2012). Context-GMM: Incremental learning of sparse priors for Gaussian mixture regression. In 2012 IEEE International Conference on Robotics and Biomimetics, ROBIO 2012 - Conference Digest (pp. 1446–1451). https://doi.org/10.1109/ROBIO.2012.6491172
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