Purpose for Open-Ended Learning Robots: A Computational Taxonomy,
  Definition, and Operationalisation
Gianluca Baldassarre Richard J. Duro Emilio Cartoni Mehdi Khamassi Alejandro Romero Vieri Giuliano Santucci
Autonomous open-ended learning (OEL) robots are able to cumulatively acquire
new skills and knowledge through direct interaction with the environment, for
example relying on the guidance of intrinsic motivations and self-generated
goals. OEL robots have a high relevance for applications as they can use the
autonomously acquired knowledge to accomplish tasks relevant for their human
users. OEL robots, however, encounter an important limitation: this may lead to
the acquisition of knowledge that is not so much relevant to accomplish the
users' tasks. This work analyses a possible solution to this problem that
pivots on the novel concept of `purpose'. Purposes indicate what the designers
and/or users want from the robot. The robot should use internal representations
of purposes, called here `desires', to focus its open-ended exploration towards
the acquisition of knowledge relevant to accomplish them. This work contributes
to develop a computational framework on purpose in two ways. First, it
formalises a framework on purpose based on a three-level motivational hierarchy
involving: (a) the purposes; (b) the desires, which are domain independent; (c)
specific domain dependent state-goals. Second, the work highlights key
challenges highlighted by the framework such as: the `purpose-desire alignment
problem', the `purpose-goal grounding problem', and the `arbitration between
desires'. Overall, the approach enables OEL robots to learn in an autonomous
way but also to focus on acquiring goals and skills that meet the purposes of
the designers and users.