# Prompting Task Trees using Gemini: Methodologies and Insights

arXiv: https://arxiv.org/abs/2405.03671 · HTML: https://mlsanity.com/paper/2405.03671/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 6 May 2024.

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

- [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)
- [Approximate Task Tree Retrieval in a Knowledge Network for Robotic Cooking](https://mlsanity.com/paper/2207.03693/) (2022)
- [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)
- [Evaluating Recipes Generated from Functional Object-Oriented Network](https://mlsanity.com/paper/2106.00728/) (2021)

## Abstract (arXiv)

Robots are the future of every technology where every advanced technology eventually will be used to make robots which are more efficient. The major challenge today is to train the robots exactly and empathetically using knowledge representation. This paper gives you insights of how we can use unstructured knowledge representation and convert them to meaningful structured representation with the help of prompt engineering which can be eventually used in the robots to make help them understand how human brain can make wonders with the minimal data or objects can providing to them.