Long-Horizon Planning and Execution With Functional Object-Oriented Networks
David Paulius, Alejandro Agostini, Dongheui Lee
Abstract
Following work on joint object-action representations, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">functional object-oriented networks</i> (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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">object-level planning</i> . 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.
BibTeX
@inproceedings{ral2023_longhorizonplann,
title = {Long-Horizon Planning and Execution With Functional Object-Oriented Networks},
author = {David Paulius and Alejandro Agostini and Dongheui Lee},
booktitle = {RA-L 2023},
year = {2023}
}