# Approximate Task Tree Retrieval in a Knowledge Network for Robotic Cooking

arXiv: https://arxiv.org/abs/2207.03693 · HTML: https://mlsanity.com/paper/2207.03693/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2022.

## Built on by (most cited first, 13 of 13)

- [ManipDreamer: Boosting Robotic Manipulation World Model with Action Tree and Visual Guidance](https://mlsanity.com/paper/2504.16464/) (2025)
- [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)
- [DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physics](https://mlsanity.com/paper/2312.06408/) (2023)
- [Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability](https://mlsanity.com/paper/2401.07868/) (2024)
- [AdaptBot: Combining LLM with Knowledge Graphs and Human Input for Generic-to-Specific Task Decomposition and Knowledge Refinement](https://mlsanity.com/paper/2502.02067/) (2025)
- [STAR: A Foundation Model-driven Framework for Robust Task Planning and Failure Recovery in Robotic Systems](https://mlsanity.com/paper/2503.06060/) (2025)
- [FOON Creation and Traversal for Recipe Generation](https://mlsanity.com/paper/2210.07335/) (2022)
- [Knowledge Retrieval using Functional Object-Oriented Network](https://mlsanity.com/paper/2211.03037/) (2022)
- [Knowledge Retrieval](https://mlsanity.com/paper/2211.03522/) (2022)
- [Knowledge Retrieval using Foon](https://mlsanity.com/paper/2211.03790/) (2022)
- [Knowledge Retrieval for Robotic Cooking](https://mlsanity.com/paper/2211.04524/) (2022)
- [Knowledge Retrieval Using Functional Object-Oriented Networks](https://mlsanity.com/paper/2211.14896/) (2022)
- [Prompting Task Trees using Gemini: Methodologies and Insights](https://mlsanity.com/paper/2405.03671/) (2024)

## Abstract (arXiv)

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.