  Flexible task planning continues to pose a difficult challenge for robots,
where a robot is unable to creatively adapt their task plans to new or unseen
problems, which is mainly due to the limited knowledge it has about its actions
and world. Motivated by a human's ability to adapt, we explore how task plans
from a knowledge graph, known as the Functional Object- Oriented Network
(FOON), can be generated for novel problems requiring concepts that are not
readily available to the robot in its knowledge base. Knowledge from 140
cooking recipes are structured in a FOON knowledge graph, which is used for
acquiring task plan sequences known as task trees. Task trees can be modified
to replicate recipes in a FOON knowledge graph format, which can be useful for
enriching FOON with new recipes containing unknown object and state
combinations, by relying upon semantic similarity. We demonstrate the power of
task tree generation to create task trees with never-before-seen ingredient and
state combinations as seen in recipes from the Recipe1M+ dataset, with which we
evaluate the quality of the trees based on how accurately they depict newly
added ingredients. Our experimental results show that our system is able to
provide task sequences with 76% correctness.
