Dynamic Movement Primitives ( DMPs ) encode a desired movement trajectory in terms of the attractor

Dynamic Movement Primitives have successfully been used to realize imitation learning, trial-and-error learning, reinforcement learning, movement recognition and segmentation and control. Because of this they have become a popular representation 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 representation of DMPs, algorithms such as Kalman filtering and smoothing are directly applicable to perform inference on proprioceptive sensor measurements during execution. We show that inference in this probabilistic model automatically leads to a feedback term to online modulate the execution of a DMP. Furthermore, we show how inference allows us to measure the likelihood that we are successfully executing a given motion primitive. In this context, we show initial results of using the probabilistic model to detect execution failures on a simulated movement primitive dataset.