---
library_name: transformers
pipeline_tag: text-generation
base_model: meta-llama/Llama-3.2-1B-Instruct
license: llama3.2
language:
- en
tags:
- robotics
- behavior-trees
- behaviortree-cpp
- ros2
- llama
- arxiv:2602.01870
model-index:
- name: BTGenBot-2
  results:
  - task:
      type: text-generation
      name: Behavior Tree Generation
    dataset:
      name: BT Benchmark
      type: custom
    metrics:
    - type: success_rate
      name: Zero-shot Success Rate with Error Recovery
      value: 90.38
      unit: '%'
    - type: success_rate
      name: One-shot Success Rate with Error Recovery
      value: 98.07
      unit: '%'
    - type: xml_syntax
      name: XML Syntax Correctness with Error Recovery
      value: 100
      unit: '%'
    - type: action_coherency
      name: Action Coherency with Error Recovery
      value: 100
      unit: '%'
datasets:
- AIRLab-POLIMI/synthetic_bt
---

# BTGenBot-2

`AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt` is the fine-tuned Llama 3.2 1B Instruct model released with **BTGenBot-2: Efficient Behavior Tree Generation with Small Language Models**.

BTGenBot-2 generates executable robot **Behavior Trees** from:

1. a natural-language task description, and
2. a list of available robot action primitives.

The model outputs XML Behavior Trees compatible with **BehaviorTree.CPP**, supporting ROS 2 robotics behavior-tree pipelines.

For the complete project, examples, code, dataset, benchmark, and paper, visit:

👉 **https://airlab-polimi.github.io/BTGenBot-2/**

## Model Details

- **Developed by:** AIRLab, Politecnico di Milano
- **Authors:** Riccardo Andrea Izzo, Gianluca Bardaro, Matteo Matteucci
- **Base model:** `meta-llama/Llama-3.2-1B-Instruct`
- **Model type:** Small language model for Behavior Tree generation
- **Fine-tuning method:** QLoRA / LoRA parameter-efficient fine-tuning
- **Input:** Natural-language robot task + available robot action primitives
- **Output:** XML Behavior Tree compatible with BehaviorTree.CPP
- **Language:** English
- **Project page:** https://airlab-polimi.github.io/BTGenBot-2/
- **Paper:** https://arxiv.org/abs/2602.01870
- **Code:** https://github.com/AIRLab-POLIMI/BTGenBot-2

## Intended Use

This model is intended to generate robot Behavior Trees for research and development in robotics task planning.

Example applications include:

- ROS 2 / Nav2-compatible task planning;
- navigation Behavior Tree generation;
- manipulation Behavior Tree generation;
- simulation-based robot-task validation;
- benchmarking language-model-based Behavior Tree generation.

## Input Format

The recommended input format is:

```text
Task:
Describe the robot task in natural language.

Actions:
[ActionName(parameters: parameter_1, parameter_2), AnotherAction(parameters: parameter_1)]
```

## Output Format

The model is expected to return XML only:

```xml
<root BTCPP_format="4">
  <BehaviorTree ID="MainTree">
    ...
  </BehaviorTree>
</root>
```

## Training Data

BTGenBot-2 was trained on a synthetic instruction-following dataset of **5,204** natural-language instruction / Behavior Tree pairs.

Each sample contains:

- `instruction`: system-level instructions for Behavior Tree generation;
- `input`: task description and available robot actions;
- `output`: XML Behavior Tree.

The dataset was generated from real Behavior Trees and expanded through controlled synthetic generation.

See the full project page for details:

https://airlab-polimi.github.io/BTGenBot-2/

## Training Procedure

The model was fine-tuned from `meta-llama/Llama-3.2-1B-Instruct` using QLoRA / LoRA.

Reported training details from the paper include:

- **Train/test split:** 95% / 5%
- **Learning rate:** `1e-4`
- **Warmup ratio:** `0.1`
- **Batch size:** `16`
- **Training duration:** approximately 30 hours
- **Hardware:** 2 × NVIDIA RTX Quadro 6000 GPUs, 48 GB total VRAM

## Citation

```bibtex
@article{izzo2026btgenbot,
  title={BTGenBot-2: Efficient Behavior Tree Generation with Small Language Models},
  author={Izzo, Riccardo Andrea and Bardaro, Gianluca and Matteucci, Matteo},
  journal={arXiv preprint arXiv:2602.01870},
  year={2026}
}
```

## More Information

- **Project page:** https://airlab-polimi.github.io/BTGenBot-2/
- **Code:** https://github.com/AIRLab-POLIMI/BTGenBot-2
- **Paper:** https://arxiv.org/abs/2602.01870