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While many quality metrics exist to evaluate the quality of a grasp byitself, no clear quantification of the quality of a grasp relatively to thetask the grasp is used for has been defined yet. In this paper we propose aframework to extend the concept of grasp quality metric to task-orientedgrasping by defining affordance functions via basic grasp metrics for an openset of task affordances. We evaluate both the effectivity of the proposed taskoriented metrics and their practical applicability by learning to infer themfrom vision. Indeed, we assess the validity of our novel framework both in thecontext of perfect information, i.e., known object model, and in the partialinformation 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 inknown object model simulation, in the latter deep models are trained to infersuch properties from partial information in the form of synthesized rangeimages.

Towards Affordance Prediction with Vision via Task Oriented Grasp Quality Metrics