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Dynamic Movement Primitives (DMPs) represent stable goal-directed or periodic movements, which are learned from observations or demonstrations. They rely on proper function approximators, which are sufficiently flexible to represent arbitrary movements but also ensure goal convergence in pointto-point motions. This work shows that Gaussian Processes (GPs) are suitable as a regressor for learning movements with DMPs ensuring stability. In addition, GPs provide a measure for the uncertainty aboutdoi:10.1109/iros.2016.7759576 dblp:conf/iros/FangerUH16 fatcat:pqvldev5gvg3rf2pa7w2xtisui