  While grasps must satisfy the grasping stability criteria, good grasps depend
on the specific manipulation scenario: the object, its properties and
functionalities, as well as the task and grasp constraints. In this paper, we
consider such information for robot grasping by leveraging manifolds and
symbolic object parts. Specifically, we introduce a new probabilistic logic
module to first semantically reason about pre-grasp configurations with respect
to the intended tasks. Further, a mapping is learned from part-related visual
features to good grasping points. The probabilistic logic module makes use of
object-task affordances and object/task ontologies to encode rules that
generalize over similar object parts and object/task categories. The use of
probabilistic logic for task-dependent grasping contrasts with current
approaches that usually learn direct mappings from visual perceptions to
task-dependent grasping points. We show the benefits of the full probabilistic
logic pipeline experimentally and on a real robot.
