  Current robotic manipulation requires reliable methods to predict whether a
certain grasp on an object will be successful or not prior to its execution.
Different methods and metrics have been developed for this purpose but there is
still work to do to provide a robust solution.
  In this article we combine different metrics to evaluate real grasp
executions. We use different machine learning algorithms to train a classifier
able to predict the success of candidate grasps. Our experiments are performed
with two different robotic grippers and different objects. Grasp candidates are
evaluated in both simulation and real world.
  We consider 3 different categories to label grasp executions: robust, fragile
and futile. Our results shows the proposed prediction model has success rate of
76\%.
