  While many quality metrics exist to evaluate the quality of a grasp by
itself, no clear quantification of the quality of a grasp relatively to the
task the grasp is used for has been defined yet. In this paper we propose a
framework to extend the concept of grasp quality metric to task-oriented
grasping by defining affordance functions via basic grasp metrics for an open
set of task affordances. We evaluate both the effectivity of the proposed task
oriented metrics and their practical applicability by learning to infer them
from vision. Indeed, we assess the validity of our novel framework both in the
context of perfect information, i.e., known object model, and in the partial
information context, i.e., inferring task oriented metrics from vision,
underlining advantages and limitations of both situations. In the former,
physical metrics of grasp hypotheses on an object are defined and computed in
known object model simulation, in the latter deep models are trained to infer
such properties from partial information in the form of synthesized range
images.
