GPT-Planner

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Paper: Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds

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# Common sense-based Open-World Planning (COWP)

[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/facebookresearch/home-robot/blob/main/LICENSE)
[![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://www.python.org/downloads/release/python-370/)

Please note that OpenAI regularly updates their APIs for accessing GPT responses. If the API mentioned in this repository is outdated, kindly update the corresponding APIs on your own.

## Colab Version (new)
We highly recommend using the Colab version.

One of main difference is it generates PDDL code using a third-party package called pddl (https://github.com/AI-Planning/pddl).

By utilizing this package, it becomes simpler to inject knowledge from LLMs into the classical planner.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1NLo7W2TpfZEpZ3GG3uxbl1q9ul1bBDrA?usp=sharing)

## Environment
ubuntu (version 16.04, 18.04, and 20.04 have been tested) <br />

## Install GPT-Planner
### step 1: download GPT-Planner
* open terminal
* `cd`
*  `git clone https://github.com/yding25/GPT-Planner.git`

Note that please place the GPT-planner folder in your home folder.
### step 2: install dependencies
* `pip install numpy==1.19.5`
*  `pip install openai==0.8.0`
### step 3: install FastDownward (detailed instruction at https://www.fast-downward.org/ObtainingAndRunningFastDownward)
* `cd GPT-Planner`
* `git clone https://github.com/aibasel/downward.git FastDownward`
* `sudo apt install cmake g++ git make python3`
* `cd FastDownward`
* `python build.py`
* `cd ..`
### step 4: prepare an GPT-3 API key
Here is a brief instruction to obtain a key. 
* `log in https://beta.openai.com/`
* `click the icon ``Personal'' on the top right`
* `click ``view API keys''`
* `click ``+ Create new secret key''`
* `copy the key and paste it in GPT-Planner/dataset/openai_api_key.txt`

## Run GPT-Planner
* change the value of `task_id` (line 19 in `main.py` file) to select an task
```
task_id = 4
task = {
    1: 'cleaning floor',
    4: 'drinking water',
    6: 'setting table',
    9: 'grasping plate',
    10: 'drinking soda',
    11: 'eating dinner'
    }
task_name = task[task_id]
```

* `python main.py`

Here are one trial result, where the left and right figures show the situation and solution, respectively. The situation is "the cup_1 is missing", and the solution is that GPT-3 suggests using a drinking glasss to replace the cup in the task.
<img src="https://github.com/yding25/GPT-Planner/blob/master/dataset/situation.png" width="57%" height="45%">

## Experiment Results
All experiment results (log files) can be found in the folder "/GPT-Planner/results". For example, the below figure shows situation0 in task4 has been handled in some trials, and some are not.

## Citation and Reference
```
@article{ding2023integrating,
  title={Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds},
  author={Ding, Yan and Zhang, Xiaohan and Amiri, Saeid and Cao, Nieqing and Yang, Hao and Kaminski, Andy and Esselink, Chad and Zhang, Shiqi},
  journal={arXiv preprint arXiv:2305.17590},
  year={2023}
}
@article{ding2022robot,
  title={Robot task planning and situation handling in open worlds},
  author={Ding, Yan and Zhang, Xiaohan and Amiri, Saeid and Cao, Nieqing and Yang, Hao and Esselink, Chad and Zhang, Shiqi},
  journal={arXiv preprint arXiv:2210.01287},
  year={2022}
}
```

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