  We present a novel, reusable and task-agnostic primitive for assessing the
outcome of a force-interaction robotic skill, useful e.g.\ for applications
such as quality control in industrial manufacturing. The proposed method is
easily programmed by kinesthetic teaching, and the desired adaptability and
reusability are achieved by machine learning models. The primitive records
sensory data during both demonstrations and reproductions of a movement.
Recordings include the end-effector's Cartesian pose and exerted wrench at each
time step. The collected data are then used to train Gaussian Processes which
create models of the wrench as a function of the robot's pose. The similarity
between the wrench models of the demonstration and the movement's reproduction
is derived by measuring their Hellinger distance. This comparison creates
features that are fed as inputs to a Naive Bayes classifier which estimates the
movement's probability of success. The evaluation is performed on two diverse
robotic assembly tasks -- snap-fitting and screwing -- with a total of 5 use
cases, 11 demonstrations, and more than 200 movement executions. The
performance metrics prove the proposed method's capability of generalization to
different demonstrations and movements.
