機能的对象指向ネットワークによる長期計画と実行
Long-Horizon Planning and Execution with Functional Object-Oriented Networks
FOONをPDDLに自動変換し、階層的計画パイプラインで長期タスクを実行する手法を提案し、CoppeliaSimで検証した。
著者: David Paulius, Alejandro Agostini, Dongheui Lee
分類: cs.RO, cs.AI
原文アブストラクト
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.
