  Affordances describe the possibilities for an agent to perform actions with
an object. While the significance of the affordance concept has been previously
studied from varied perspectives, such as psychology and cognitive science,
these approaches are not always sufficient to enable direct transfer, in the
sense of implementations, to artificial intelligence (AI)-based systems and
robotics. However, many efforts have been made to pragmatically employ the
concept of affordances, as it represents great potential for AI agents to
effectively bridge perception to action. In this survey, we review and find
common ground amongst different strategies that use the concept of affordances
within robotic tasks, and build on these methods to provide guidance for
including affordances as a mechanism to improve autonomy. To this end, we
outline common design choices for building representations of affordance
relations, and their implications on the generalisation capabilities of an
agent when facing previously unseen scenarios. Finally, we identify and discuss
a range of interesting research directions involving affordances that have the
potential to improve the capabilities of an AI agent.
