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Handling high parameter dimensionality in reinforcement learning with dynamic motor primitives
Colomé Figueras, Adrià; Alenyà Ribas, Guillem; Torras, Carme
Universitat Politècnica de Catalunya. Institut de Robòtica i Informàtica Industrial; Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
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.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Robòtica
Intelligent Robots and Computer Vision
intelligent robots
robot kinematics
robot programming Author keywords: dynamic motor primitives
learning by demonstration
Classificació INSPEC::Automation::Robots::Intelligent robots
Attribution-NonCommercial-NoDerivs 3.0 Spain

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