  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 directly applicable to perform
inference on pro- prioceptive 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.
