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.

翻译：自主开放式学习机器人能够通过与环境的直接交互逐步积累新技能和知识，例如依赖内在动机和自我生成目标的引导。开放式学习机器人具有重要的应用价值，因为它们可以利用自主获取的知识完成与人类用户相关的任务。然而，开放式学习机器人面临一个关键局限：这可能导致获取的知识与用户任务的完成相关性不足。本文分析了一种可能的解决方案，该方案围绕“目的”这一新概念展开。目的表示设计者和/或用户对机器人的期望。机器人应利用目的的内部表征（此处称为“愿望”）来聚焦其开放式探索，以获取与完成这些目的相关的知识。本文从两个方面为构建关于目的的计算框架做出贡献：首先，基于三层动机层级结构形式化了关于目的的框架，该层级包括：(a) 目的；(b) 领域无关的愿望；(c) 特定领域的状态目标。其次，本文强调了该框架揭示的关键挑战，例如：“目的-愿望对齐问题”、“目的-目标落地问题”以及“愿望间的仲裁”。总体而言，该方法使开放式学习机器人不仅能够以自主方式学习，还能专注于获取满足设计者和用户目的的目标与技能。