--- license: gpl-3.0 pipeline_tag: robotics --- ![](doc/img/logo/logo8.png) --- # 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。**