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：继联合对象-动作表征研究之后，功能性对象导向网络（FOON）被提出作为一种机器人知识图谱表征方法。FOON包含对机器人理解任务及环境、执行对象级规划具有重要意义的符号化概念。在此工作之前，由于FOON中的概念过于抽象而难以执行，鲜有研究展示如何将FOON生成的规划由机器人实际执行。为此，我们提出利用FOON中的对象级知识进行任务规划与执行的思路。该方法自动将FOON转换为PDDL语言，并采用分层规划管道，结合现成规划器、动作上下文及机器人技能，生成可执行的任务规划。我们在CoppeliaSim仿真环境中对长时域任务进行了完整演示，并展示了如何将学习到的动作上下文泛化至未见场景。