# Task Tree Retrieval for Robotic Cooking

arXiv: https://arxiv.org/abs/2211.01745 · HTML: https://mlsanity.com/paper/2211.01745/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 3 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images](https://mlsanity.com/paper/1810.06553/) (2018)
- [Functional Object-Oriented Network for Manipulation Learning](https://mlsanity.com/paper/1902.01537/) (2019)
- [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)

## Abstract (arXiv)

Robotics is used to foster creativity. Humans can perform jobs in their unique manner, depending on the circumstances. This situation applies to food cooking. Robotic technology in the kitchen can speed up the process and reduce its workload. However, the potential of robotics in the kitchen is still unrealized. In this essay, the idea of FOON, a structural knowledge representation built on insights from human manipulations, is introduced. To reduce the failure rate and ensure that the task is effectively completed, three different algorithms have been implemented where weighted values have been assigned to the manipulations depending on the success rates of motion. This knowledge representation was created using videos of open-sourced recipes