Paulius, D., Agostini, A., & Lee, D. (2023). Long-Horizon Planning and Execution With Functional Object-Oriented Networks. IEEE Robotics and Automation Letters, 8(8), 4513–4520. https://doi.org/10.1109/LRA.2023.3285510
learning from demonstration; manipulation planning; service robotics; Task and motion planning
en
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.
