Robotic cognitive manipulation methods aim to
imitate the human-object interactive process. Most of the of
the state-of-the-art literature explore these methods by focusing
on the target object or on the robot’s morphology, without including
the surrounding environment. Most recent approaches
suggest that taking into account the semantic properties of
the surrounding environment improves the object recognition.
When it comes to human cognitive development methods, these
physical qualities are not only inferred from the object but also
from the semantic characteristics of the surroundings. Thus the
importance of affordances. In affordances, the representation
of the perceived physical qualities of the objects gives valuable
information about the possible manipulation actions. Hence,
our research pursuits to develop a cognitive affordances map
by (i) considering the object and the characteristics of the
environment in which this object is more likely to appear,
and (ii) achieving a learning mechanism that will intrinsically
learn these affordances from self-experience.