Incremental Semiparametric Inverse Dynamics Learning

January 18, 2016 Β· Declared Dead Β· πŸ› IEEE International Conference on Robotics and Automation

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Authors Raffaello Camoriano, Silvio Traversaro, Lorenzo Rosasco, Giorgio Metta, Francesco Nori arXiv ID 1601.04549 Category stat.ML: Machine Learning (Stat) Cross-listed cs.LG, cs.RO Citations 51 Venue IEEE International Conference on Robotics and Automation Last Checked 2 months ago
Abstract
This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical properties of the system. This yields to an incremental semiparametric approach, leveraging the advantages of both the parametric and nonparametric models. We validate the proposed technique learning the dynamics of one arm of the iCub humanoid robot.
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