ABIL-KDD-2025

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[KDD 2025] Official implementation of ABIL

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# ABIL: Learning for Long-Horizon Planning via Neuro-Symbolic Abductive Imitation

### [Paper](https://arxiv.org/abs/2411.18201) | [Project Page](https://www.lamda.nju.edu.cn/shaojj/KDD25_ABIL/)

## News
- **2024.12.30** Release code.
- **2024.12.22** Release training data.
- **2024.11.17** ABIL is accepted by **KDD 2025**!

## The Framework of Abductive Imitation Learning.

![ABIL](https://raw.githubusercontent.com/Hoar012/ABIL-KDD-2025/main/images/framework.png)

## Contents

- [Install](#install)
- [Data](#data)
- [Train](#train)
- [Evaluation](#evaluation)

### Install

1. Clone the repo into a local folder.

```bash
git clone https://github.com/Hoar012/ABIL-KDD-2025.git

cd ABIL-KDD-2025
conda create -n ABIL python=3.8
conda activate ABIL
pip install -r requirements.txt
```

2. Clone the Jacinle repo.

```bash
git clone https://github.com/vacancy/Jacinle --recursive
export PATH=<path_to_jacinle>/bin:$PATH
```

#### mini-behavior environment
``` bash
cd ./hacl/envs/mini_behavior
pip install -e .
```

#### Cliport environment
```bash
cd ./hacl/envs/cliport
export CLIPORT_ROOT=$(pwd)
python setup.py develop
```

### Data
Our training demonstrations are generated by Python scripts. View them separately in the following files:

- BabyAI: `hacl/p/kfac/minigrid/data_generator.py`
- Mini-BEHAVIOR: `hacl/p/kfac/minibehavior/data_generator.py`
- CLIPort: `cliport_src/data_generator.py`

### Train

**BabyAI**

1. Train the grounding model.

```bash
jac-run babyai_src/train-babyai-abl.py minigrid goto  --use-offline=yes --structure-mode abl --action-loss-weight 1 --evaluate-interval 0 --iterations 1000 --append-expr
```

2. Train the Imitation Learning model.

```bash
jac-run babyai_src/babyai-abil-bc.py minigrid goto  --seed 33 --iterations 1000  --append-expr --load_domain dumps/abl-unlock33-load=scratch.pth
```

### Evaluation

```bash
jac-run babyai_src/babyai-abil-bc.py minigrid goto  --seed 33 --iterations 1000  --append-expr --load_domain dumps/abl-unlock33-load=scratch.pth --load dumps/seed33/abil-bc-goto-load=scratch.pth --evaluate
```

## BibTeX

```
@inproceedings{shao2025abductive,
  title={Abductive Learning for Neuro-Symbolic Grounded Imitation},
  author={Shao, Jie-Jing and Hao, Hao-Ran and Yang, Xiao-Wen and Li, Yu-Feng},
  booktitle={Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 1},
  pages={1221--1232},
  year={2025}
}
```

## Acknowledgement
[PDSketch](https://github.com/vacancy/PDSketch-Alpha-Release), [BabyAI](https://github.com/mila-iqia/babyai), [Mini-BEHAVIOR](https://github.com/StanfordVL/mini_behavior), [CLIPort](https://github.com/cliport/cliport)

ABIL-KDD-2025

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