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.

通过内在动机和自主生成的目标的引导，自主开放式学习（OEL）机器人能够通过与环境的直接交互累积地获得新的技能和知识。本研究针对 OEL 机器人在获得的知识与用户任务的相关性之间存在的问题，提出了基于 'purpose' 的概念的解决方案，并开发了一个具有三层动机层次结构的计算框架，从而使机器人能够自主学习并专注于获取与设计师和用户目的相关的目标和技能。