  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.
