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- W1540804772 abstract "Dynamic Motor Primitives (DMP) are nowadays widely used as movement parametrization for learning trajectories, because of their linearity in the parameters, rescalation robustness and continuity. However, when learning a movement with DMP, where a set of gaussians distributed along the trajectory is used to approximate an acceleration excitation function, a very large number of gaussian approximations need to be performed. Adding them up for all joints yields too many parameters to be explored, thus requiring a prohibitive number of experiments/simulations to converge to a solution with an optimal (locally or globally) reward. We propose here two strategies to reduce this dimensionality: the first is to explore only the most significant directions in the parameter space, and the second is to add a reduced second set of gaussians that should only optimize the trajectory after fixing the gaussians that approximate the demonstrated movement." @default.
- W1540804772 created "2016-06-24" @default.
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- W1540804772 date "2013-01-01" @default.
- W1540804772 modified "2023-09-27" @default.
- W1540804772 title "Handling high parameter dimensionality in reinforcement learning with dynamic motor primitives" @default.
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