Artificial Intelligence is essential to achieve a reliable human-robot
interaction, especially when it comes to manipulation
tasks. Most of the state-of-the-art literature explores
robotics grasping methods by focusing on the target object or
the robot’s morphology, without including the environment.
When it comes to human cognitive development approaches,
these physical qualities are not only inferred from the object,
but also from the semantic characteristics of the surroundings.
The same analogy can be used in robotic affordances
for improving objects grasps, where the perceived physical
qualities of the objects give valuable information about the
possible manipulation actions. This work proposes a framework
able to reason on the object affordances and grasping
regions. Each calculated grasping area is the result of a sequence
of concrete ranked decisions based on the inference
of different highly related attributes. The results show that
the system is able to infer on good grasping areas depending
on its affordance without having any a-priori knowledge on
the shape nor the grasping points.