Following work on joint object-action representation, functional object-oriented networks (FOON) were introduced as a knowledge representation for robots. A FOON contains symbolic (high-level) 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 immediate execution. We propose a hierarchical task planning approach that translates a FOON graph into a PDDL-based representation of domain knowledge for task planning and execution. As a result of this process, a task plan can be acquired, which can be executed by a robot from start to end, leveraging the use of action contexts and skills as dynamic movement primitives (DMPs). We demonstrate the entire pipeline from planning to execution using CoppeliaSim and show how learned action contexts can be extended to never-before-seen scenarios.

翻译：在联合对象-动作表示的相关研究工作之后，功能对象网络（FOON）被引入作为机器人的一种知识表示方法。FOON包含对机器人理解任务及其环境进行对象级规划有用的符号化（高层级）概念。在本研究之前，由于FOON中的概念过于抽象而无法直接执行，鲜有工作展示机器人如何执行从FOON获得的规划。我们提出一种分层任务规划方法，将FOON图转换为基于PDDL的领域知识表示形式，以支持任务规划与执行。通过这一过程，可获取完整的任务规划，该规划能够从开始到结束由机器人执行，并利用动作上下文与作为动态运动基元（DMP）的技能。我们使用CoppeliaSim演示了从规划到执行的完整流程，并展示了学习到的动作上下文如何被推广到从未见过的场景中。