Presentado al IEEE International Conference on Robotics and Automation celebrado en Karlsruhe (Alemania) del 6 al 10 de mayo de 2013. ; 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. ; This work is partially funded by EU Project IntellAct (FP7-269959) and by the Spanish Ministry of Science and Innovation under project PAU+DPI2011-27510. ; Peer Reviewed
Handling high parameter dimensionality in reinforcement learning with dynamic motor primitives
2013-01-01
Conference paper
Electronic Resource
English
DDC: | 629 |
Handling high parameter dimensionality in reinforcement learning with dynamic motor primitives
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