https://github.com/alejandromllo/action_flow_matching
https://github.com/alejandromllo/action_flow_matching
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/alejandromllo/action_flow_matching
- Owner: AlejandroMllo
- Created: 2025-04-19T12:50:50.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-19T12:52:35.000Z (over 1 year ago)
- Last Synced: 2025-04-19T17:48:06.835Z (over 1 year ago)
- Size: 1000 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Action Flow Matching (AFM)
Code for the paper **"Action Flow Matching for Continual Robot Learning"** presented at Robotics: Science and Systems (RSS) 2025.
[](https://www.roboticsproceedings.org/rss21/p026.html)
[](https://arxiv.org/abs/2504.18471)
[](https://huggingface.co/AlejandroMllo/action_flow_matching)
AFM enables efficient, non-episodic, online robot model refinement. AFM adjusts the planned actions seeking to reduce the gap between the intended and realized/actual state transitions.
---
## Table of Contents
* [Installation](#installation)
* [Conda Environment Setup](#conda-environment-setup)
* [Dependencies](#dependencies)
* [Data and Pre-trained Weights](#data-and-pre-trained-weights)
* [Experiment Execution](#experiment-execution)
* [Citation](#citation)
* [Contact](#contact)
---
## Installation
### Clone the Repository
Before setting up the environment, clone this repository and navigate to the root directory:
```bash
git clone https://github.com/AlejandroMllo/action_flow_matching.git
cd action_flow_matching
```
### Conda Environment Setup
We recommend creating a dedicated Conda environment for this project.
1. **Create the environment:**
```bash
conda create -n afm python=3.10.16 -y
```
2. **Activate the environment:**
```bash
conda activate afm
```
### Dependencies
Install required Python packages:
```bash
pip install -r requirements.txt
```
---
## Data and Pre-trained Weights
All required datasets and pre-trained model weights are hosted on Hugging Face (https://huggingface.co/AlejandroMllo/action_flow_matching):
1. **Install Hugging Face Hub CLI:**
```bash
pip install huggingface_hub
```
2. **Download Models:** From the project's root, type:
```bash
hf download AlejandroMllo/action_flow_matching --exclude data/* --local-dir .
```
3. **[Optional] Download Dataset:**
```bash
hf download AlejandroMllo/action_flow_matching --exclude artifacts/* --local-dir .
```
After downloading the pretrained checkpoints and (optionally) the datasets, your project root should look like:
```
.
├── .git/
├── .gitignore
├── README.md
├── artifacts/
│ └── ugv/
│ ├── afm/
│ │ └── latest_model.pth
│ └── dynamics/
│ ├── model_0
│ ├── model_1
│ ├── model_2
│ ├── model_3
│ └── model_4
├── assets/
├── data/
│ └── ugv/
│ ├── afm_data_dubins.pt
│ ├── ugv_transition_dynamics_delta.csv
│ └── ugv_transition_dynamics_val_delta.csv
├── requirements.txt
├── results/ # If you already have any
└── src/
```
---
## Experiment Execution
Run the experiments from the `continual_dynamics_learning.py` script (note that you need to navigate to `/src`):
```bash
cd src/
python continual_dynamics_learning.py
```
The results will be automatically saved at the same level of the `/src` directory in the `/results` folder.
## Citation
If you use this code, please cite our paper:
```bibtex
@INPROCEEDINGS{Murillo-GonzálezA-RSS-25,
AUTHOR = {Alejandro Murillo-González AND Lantao Liu},
TITLE = {{Action Flow Matching for Continual Robot Learning}},
BOOKTITLE = {Proceedings of Robotics: Science and Systems},
YEAR = {2025},
ADDRESS = {Los Angeles, CA, USA},
MONTH = {June},
DOI = {10.15607/RSS.2025.XXI.026}
}
```
## Contact
For questions or issues, please contact:
* Alejandro Murillo-Gonzalez: [almuri@iu.edu](mailto:almuri@iu.edu)
Feel free to open GitHub issues or pull requests for contributions.