Following work on joint object-action representations, functional object-oriented networks (FOON) were introduced as a knowledge graph representation for robots. A FOON contains symbolic concepts useful to a robot's understanding of tasks and its environment for object-level planning. Prior to this work, little has been done to show how plans acquired from FOON can be executed by a robot, as the concepts in a FOON are too abstract for execution. We thereby introduce the idea of exploiting object-level knowledge as a FOON for task planning and execution. Our approach automatically transforms FOON into PDDL and leverages off-the-shelf planners, action contexts, and robot skills in a hierarchical planning pipeline to generate executable task plans. We demonstrate our entire approach on long-horizon tasks in CoppeliaSim and show how learned action contexts can be extended to never-before-seen scenarios.

翻译：在联合对象-动作表征研究的基础上，功能对象网络（FOON）被引入作为面向机器人的知识图谱表征。FOON包含对机器人理解任务及环境进行对象级规划具有重要意义的符号化概念。此前工作尚未充分展示如何使机器人执行从FOON获取的规划方案，原因在于FOON中的概念过于抽象难以直接执行。为此，我们提出利用FOON中蕴含的对象级知识进行任务规划与执行的新思路。该方法自动将FOON转化为PDDL语言，并利用现成规划器、动作上下文以及机器人技能构建分层规划流水线，从而生成可执行的任务规划方案。我们在CoppeliaSim仿真环境中针对长期任务验证了完整方法体系，并展示了如何将习得的动作上下文扩展应用于未见过的全新场景。