Modelling Smooth Paths Using Gaussian Processes

Christopher Tay 1 Christian Laugier 1
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : A generative model based on the gaussian mixture model and gaussian processes is presented in this paper. Typical motion paths are learnt and then used for motion prediction using this model. The principal novel aspect of this approach is the modelling of paths using gaussian processes. It allows the representation of smooth trajectories and avoids discretization problems found in most existing methods. Gaussian processes not only provides a comprehensive and formal theoretical framework to work with, it also lends itself naturally to path clustering using gaussian mixture models. Learning is performed using expectation maximization where the E-Step uses variational methods to maximize its lower bound before optimization over parameters are performed in the M-Step.
keyword : Gaussian Process
Type de document :
Communication dans un congrès
Proc. of the Int. Conf. on Field and Service Robotics, 2007, Chamonix, France. 2007
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Contributeur : Christian Laugier <>
Soumis le : mercredi 24 octobre 2007 - 16:47:21
Dernière modification le : jeudi 11 janvier 2018 - 06:21:47
Document(s) archivé(s) le : lundi 12 avril 2010 - 00:15:44



  • HAL Id : inria-00181664, version 1



Christopher Tay, Christian Laugier. Modelling Smooth Paths Using Gaussian Processes. Proc. of the Int. Conf. on Field and Service Robotics, 2007, Chamonix, France. 2007. 〈inria-00181664〉



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