LLM-OBTEA

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Source codes for the paper "Integrating Intent Understanding and Optimal Behavior Planning for Behavior Tree Generation from Human Instructions" (IJCAI 2024)

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# Integrating Intent Understanding and Optimal Behavior Planning for Behavior Tree Generation from Human instructions (IJCAI 2024)

[[Website]](https://dids-ei.github.io/Project/LLM-OBTEA/) [[arXiv]](https://arxiv.org/pdf/2405.07474) [[Code]](https://github.com/DIDS-EI/LLM-OBTEA)

## 📰 News

- **[2026-06]** 🎉 **MRBTP-demo is now online!** A slim, tutorial-friendly version of MRBTP has been released at <https://github.com/Caiyishuai/MRBTP-demo>. Great for quickly learning multi-robot behavior-tree planning and porting it to your own tasks.
- **[2025-02]** 🏆 **MRBTP** (AAAI 2025 **Oral**) has been released. MRBTP extends our single-robot behavior-tree planning line of work to **multi-robot** collaboration with theoretical soundness & completeness guarantees.
  - Project page: <https://dids-ei.github.io/Project/MRBTP/>
  - Code: <https://github.com/DIDS-EI/MRBTP>
  - Demo / Tutorial: <https://github.com/Caiyishuai/MRBTP-demo>
  - Paper: <https://arxiv.org/abs/2502.18072>
- **[2025-01]** ✨ The slim demo version of this project is available at <https://github.com/DIDS-EI/OBTEA-demo>.

This repository contains the code for our project, which is divided into three main sections:

1. **LLM Folder**: This directory contains the code for the Large Language Models (LLM). It encompasses all the scripts and modules necessary to run and implement the large-scale language model functionalities of our project.

2. **OBTEA Folder**: Here, you will find the code for the Behavior Tree Backward Expansion Algorithm (OBTEA). This section includes the implementation details and the necessary scripts for executing the behavior tree-based algorithms.

3. **RoboWaiter Folder**:  It contains code for deploying our algorithms in a café's digital twin scenario, showcasing the practical application of our research in simulating and enhancing café service operations.

👉 [Download Simulator](https://drive.google.com/file/d/1ayAQZbPOyQV2W-V_ZdYv6AoqLOg0zvm1/view?usp=sharing)

We are committed to continuously updating and improving this repository, so stay tuned for future enhancements and additions.

✨️ We have uploaded the slim version at `https://github.com/DIDS-EI/OBTEA-demo`. If you want to generate a BT file for a custom task, you can refer to `OBTEA-demo/test_demo/run_demo_task.py`.
1. First, create your own environment under `OBTEA-demo\btpg\envs`, such as `DemoEasy`. The key is to establish the action classes and condition classes in `exec_lib`. Pay attention to the preconditions (`pre`), additions (`add`), deletions (`del`), and their optional parameters for each action.
2. In the main function `run_demo_task.py`, provide the path to `exec_lib` to import `behavior_lib`.
3. Before running the BT algorithm, specify the goal and the current state `cur_cond_set`.
4. To draw the BT, you need the `.btml` file and the imported `behavior_lib`.
We will continue to update and maintain this project, so stay tuned!

## Related Projects

- **BTExpansion-demo**: A concise, well-documented demo that reproduces the Behavior Tree Expansion algorithm (AAAI 2021), with PTML export and Graphviz visualization. Useful as a minimal entry point to understand the backward expansion algorithm.
  - https://github.com/Caiyishuai/BTExpansion-demo

## License

This repository is released under the MIT license as found in the [LICENSE](LICENSE) file.

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