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# Scaling Up Natural Language Understanding for Multi-Robots Through the Lens of Hierarchy
[[Homepage](https://nl2hltl.github.io/)] [[Paper](https://arxiv.org/abs/2408.08188)] [[Video](https://youtu.be/o8CRrVK9g9Q)] [[Poster](https://nl2hltl.github.io/media/figures/overview.png)]
> The associated repo for paper "Scaling Up Natural Language Understanding for Multi-Robots Through the Lens of Hierarchy".
## Introduction
The dataset is modified based on the following projects, in which we replace the task related descriptions, into task independent descriptions
task related NL2TL example (from [Efficient-Eng-2-LTL](https://github.com/UM-ARM-Lab/Efficient-Eng-2-LTL))
```json
"globally ( and ( until ( scan , not ( any cubes ) ) , finally ( any cubes ) ) )": {
"formula": "globally ( and ( until ( scan , not ( any cubes ) ) , finally ( any cubes ) ) )",
"raw": "G & U S ! A F A"
},
```
task independent NL2TL example:
```json
{"natural": "go through the P01 until you get to the P04", "raw_ltl": "F ( P01 A ( F P04 ) )"}
```
**NOTE:** We mechanically obtain task independent descriptions from task related descriptions by noun/phrase substitution. Due to the removal of semantic information, some NL2TL mappings obtained through this method are not unique.
Based task related NL2TL datasets:
- datasets
- [Efficient-Eng-2-LTL](https://github.com/UM-ARM-Lab/Efficient-Eng-2-LTL)
- [Lang2LTL](https://github.com/h2r/Lang2LTL)
- [nl2spec](https://github.com/realChrisHahn2/nl2spec)
- [NL2TL](https://github.com/yongchao98/NL2TL)
## File Structure
- NL2HLTLTranslator
- fastapi_server.py a FastAPI server for translate testing, will run on localhost:8001
- mistral7b
- finetune.py code for fintune
- prediction.py code for prediction (this version do not have sockets)
- mistral7b_quat8: a fintuned model based on Mistral7B in quat 8
- NL2TL-dataset: used dataset
## Run
```bash
cd to/this/folder
pip install -e .
python finetune/fastapi_server.py
```
## Cite
```bibtex
@misc{xu2024scalingnaturallanguageunderstanding,
title={Scaling Up Natural Language Understanding for Multi-Robots Through the Lens of Hierarchy},
author={Shaojun Xu and Xusheng Luo and Yutong Huang and Letian Leng and Ruixuan Liu and Changliu Liu},
year={2024},
eprint={2408.08188},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2408.08188},
}
```
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