# A Road-map to Robot Task Execution with the Functional Object-Oriented Network

arXiv: https://arxiv.org/abs/2106.00158 · HTML: https://mlsanity.com/paper/2106.00158/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [Functional Object-Oriented Network for Manipulation Learning](https://mlsanity.com/paper/1902.01537/) (2019)
- [A Survey of Knowledge Representation in Service Robotics](https://mlsanity.com/paper/1807.02192/) (2018)
- [Functional Object-Oriented Network: Construction & Expansion](https://mlsanity.com/paper/1807.02189/) (2018)
- [Joint Object and State Recognition using Language Knowledge](https://mlsanity.com/paper/1905.08843/) (2019)
- [Long Activity Video Understanding using Functional Object-Oriented Network](https://mlsanity.com/paper/1807.00983/) (2018)

## Built on by (most cited first, 1 of 1)

- [Long-Horizon Planning and Execution with Functional Object-Oriented Networks](https://mlsanity.com/paper/2207.05800/) (2022)

## Abstract (arXiv)

Following work on joint object-action representations, the functional object-oriented network (FOON) was introduced as a knowledge graph representation for robots. Taking the form of a bipartite graph, a FOON contains symbolic or high-level information that would be pertinent to a robot's understanding of its environment and tasks in a way that mirrors human understanding of actions. In this work, we outline a road-map for future development of FOON and its application in robotic systems for task planning as well as knowledge acquisition from demonstration. We propose preliminary ideas to show how a FOON can be created in a real-world scenario with a robot and human teacher in a way that can jointly augment existing knowledge in a FOON and teach a robot the skills it needs to replicate the demonstrated actions and solve a given manipulation problem.