Trajectory prediction: learning to map situations to robot trajectories

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Trajectory prediction: learning to map situations to robot trajectories
Trajectory planning and optimization is a fundamental problem in articulated robotics. Algorithms used typically for this problem compute optimal trajectories from scratch in a new situation. In effect, extensive data is accumulated containing situations together with the respective optimized trajectories ? but this data is in practice hardly exploited. The aim of this paper is to learn from this data. Given a new situation we want to predict a suitable trajectory which only needs minor refinement by a conventional optimizer. Our approach has two essential ingredients. First, to generalize from previous situations to new ones we need an appropriate situation descriptor ? we propose a sparse feature selection approach to find such wellgeneralizing features of situations. Second, the transfer of previously optimized trajectories to a new situation should not be made in joint angle space ? we propose a more efficient task space transfer of old trajectories to new situations. Experiments ...
Nikolay Jetchev, Marc Toussaint
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2009
Where ICML
Authors Nikolay Jetchev, Marc Toussaint
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