Long-Horizon Planning and Execution With Functional Object-Oriented Networks

David Paulius, Alejandro Agostini, Dongheui Lee

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)4513-4520
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume8
Issue number8
DOIs
StatePublished - 1 Aug 2023
Externally publishedYes

Keywords

  • Task and motion planning
  • learning from demonstration
  • manipulation planning
  • service robotics

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