# Evaluating Recipes Generated from Functional Object-Oriented Network

arXiv: https://arxiv.org/abs/2106.00728 · HTML: https://mlsanity.com/paper/2106.00728/

## Key facts

- Cited by 7 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, 7 of 7)

- [From Cooking Recipes to Robot Task Trees -- Improving Planning Correctness and Task Efficiency by Leveraging LLMs with a Knowledge Network](https://mlsanity.com/paper/2309.09181/) (2023)
- [Counterfactual Recipe Generation: Exploring Compositional Generalization in a Realistic Scenario](https://mlsanity.com/paper/2210.11431/) (2022)
- [Functional Task Tree Generation from a Knowledge Graph to Solve Unseen Problems](https://mlsanity.com/paper/2112.02433/) (2021)
- [Calorie Aware Automatic Meal Kit Generation from an Image](https://mlsanity.com/paper/2112.09839/) (2021)
- [FOON Creation and Traversal for Recipe Generation](https://mlsanity.com/paper/2210.07335/) (2022)
- [Knowledge Retrieval for Robotic Cooking](https://mlsanity.com/paper/2211.04524/) (2022)
- [Prompting Task Trees using Gemini: Methodologies and Insights](https://mlsanity.com/paper/2405.03671/) (2024)

## Abstract (arXiv)

The functional object-oriented network (FOON) has been introduced as a knowledge representation, which takes the form of a graph, for symbolic task planning. To get a sequential plan for a manipulation task, a robot can obtain a task tree through a knowledge retrieval process from the FOON. To evaluate the quality of an acquired task tree, we compare it with a conventional form of task knowledge, such as recipes or manuals. We first automatically convert task trees to recipes, and we then compare them with the human-created recipes in the Recipe1M+ dataset via a survey. Our preliminary study finds no significant difference between the recipes in Recipe1M+ and the recipes generated from FOON task trees in terms of correctness, completeness, and clarity.