---
license: gpl-3.0
pipeline_tag: robotics
---

---
# Rofunc: The Full Process Python Package for Robot Learning from Demonstration and Robot Manipulation
> **Repository address: [https://github.com/Skylark0924/Rofunc](https://github.com/Skylark0924/Rofunc)**
> **Documentation: [https://rofunc.readthedocs.io/](https://rofunc.readthedocs.io/)**
Rofunc package focuses on the **Imitation Learning (IL), Reinforcement Learning (RL) and Learning from Demonstration (LfD)** for **(Humanoid) Robot Manipulation**. It provides valuable and convenient python functions, including
_demonstration collection, data pre-processing, LfD algorithms, planning, and control methods_. We also provide an
`IsaacGym` and `OmniIsaacGym` based robot simulator for evaluation. This package aims to advance the field by building a full-process
toolkit and validation platform that simplifies and standardizes the process of demonstration data collection,
processing, learning, and its deployment on robots.
## Citation
If you use rofunc in a scientific publication, we would appreciate citations to the following paper:
```
@software{liu2023rofunc,
title = {Rofunc: The Full Process Python Package for Robot Learning from Demonstration and Robot Manipulation},
author = {Liu, Junjia and Dong, Zhipeng and Li, Chenzui and Li, Zhihao and Yu, Minghao and Delehelle, Donatien and Chen, Fei},
year = {2023},
publisher = {Zenodo},
doi = {10.5281/zenodo.10016946},
url = {https://doi.org/10.5281/zenodo.10016946},
dimensions = {true},
google_scholar_id = {0EnyYjriUFMC},
}
```
> [!WARNING]
> **If our code is found to be used in a published paper without proper citation, we reserve the right to address this issue formally by contacting the editor to report potential academic misconduct!**
>
> **如果我们的代码被发现用于已发表的论文而没有被恰当引用,我们保留通过正式联系编辑报告潜在学术不端行为的权利。**