  This paper presents an approach for learning invariant features for object
affordance understanding. One of the major problems for a robotic agent
acquiring a deeper understanding of affordances is finding sensory-grounded
semantics. Being able to understand what in the representation of an object
makes the object afford an action opens up for more efficient manipulation,
interchange of objects that visually might not be similar, transfer learning,
and robot to human communication. Our approach uses a metric learning algorithm
that learns a feature transform that encourages objects that affords the same
action to be close in the feature space. We regularize the learning, such that
we penalize irrelevant features, allowing the agent to link what in the sensory
input caused the object to afford the action. From this, we show how the agent
can abstract the affordance and reason about the similarity between different
affordances.
