Reasoning about object affordances allows an autonomous
agent to perform generalised manipulation tasks
among object instances. While current approaches to grasp
affordance estimation are effective, they are limited to a single
hypothesis. We present an approach for detection and extraction
of multiple grasp affordances on an object via visual input. We
define semantics as a combination of multiple attributes, which
yields benefits in terms of generalisation for grasp affordance
prediction. We use Markov Logic Networks to build a knowledge
base graph representation to obtain a probability distribution
of grasp affordances for an object. To harvest the knowledge
base, we collect and make available a novel dataset that relates
different semantic attributes. We achieve reliable mappings
of the predicted grasp affordances on the object by learning
prototypical grasping patches from several examples. We show
our method’s generalisation capabilities on grasp affordance
prediction for novel instances and compare with similar methods
in the literature. Moreover, using a robotic platform, on simulated
and real scenarios, we evaluate the success of the grasping task
when conditioned on the grasp affordance prediction.