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A Probabilistic Representation for Dynamic Movement Primitives
[article]
2016
arXiv
pre-print
Dynamic Movement Primitives have successfully been used to realize imitation learning, trial-and-error learning, reinforce- ment learning, movement recognition and segmentation and control. Because of this they have become a popular represen- tation for motor primitives. In this work, we showcase how DMPs can be reformulated as a probabilistic linear dynamical system with control inputs. Through this probabilistic repre- sentation of DMPs, algorithms such as Kalman filtering and smoothing are
arXiv:1612.05932v1
fatcat:ata7urwtrvfxhacfk467fxo22i