# Rofunc **Repository Path**: Rain_redField/Rofunc ## Basic Information - **Project Name**: Rofunc - **Description**: forked - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: czli-dev - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 0 - **Created**: 2025-06-24 - **Last Updated**: 2026-02-04 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ![](doc/img/logo8.png) # Rofunc: The Full Process Python Package for Robot Learning from Demonstration and Robot Manipulation [![Release](https://img.shields.io/github/v/release/Skylark0924/Rofunc)](https://pypi.org/project/rofunc/) ![License](https://img.shields.io/github/license/Skylark0924/Rofunc?color=blue) ![](https://img.shields.io/github/downloads/skylark0924/Rofunc/total) [![](https://img.shields.io/github/issues-closed-raw/Skylark0924/Rofunc?color=brightgreen)](https://github.com/Skylark0924/Rofunc/issues?q=is%3Aissue+is%3Aclosed) [![](https://img.shields.io/github/issues-raw/Skylark0924/Rofunc?color=orange)](https://github.com/Skylark0924/Rofunc/issues?q=is%3Aopen+is%3Aissue) [![Documentation Status](https://readthedocs.org/projects/rofunc/badge/?version=latest)](https://rofunc.readthedocs.io/en/latest/?badge=latest) [![Build Status](https://img.shields.io/endpoint.svg?url=https%3A%2F%2Factions-badge.atrox.dev%2FSkylark0924%2FRofunc%2Fbadge%3Fref%3Dmain&style=flat)](https://actions-badge.atrox.dev/Skylark0924/Rofunc/goto?ref=main) > **Repository address: https://github.com/Skylark0924/Rofunc** 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 Isaac Gym-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. ![](doc/img/pipeline.png) - [Rofunc: The Full Process Python Package for Robot Learning from Demonstration and Robot Manipulation](#rofunc-the-full-process-python-package-for-robot-learning-from-demonstration-and-robot-manipulation) - [Installation](#installation) - [Install from PyPI (stable version)](#install-from-pypi-stable-version) - [Install from Source (nightly version, recommended)](#install-from-source-nightly-version-recommended) - [Documentation](#documentation) - [Star History](#star-history) - [Citation](#citation) - [Related Papers](#related-papers) - [The Team](#the-team) - [Acknowledge](#acknowledge) - [Learning from Demonstration](#learning-from-demonstration) - [Planning and Control](#planning-and-control) ## Installation ### Install from PyPI (stable version) The installation is very easy, ```shell pip install rofunc # [Option] Install with baseline RL frameworks (SKRL, RLlib, Stable Baselines3) and Envs (gymnasium[all], mujoco_py) pip install rofunc[baselines] ``` and as you'll find later, it's easy to use as well! ```python import rofunc as rf ``` Thus, have fun in the robotics world! > **Note** > Several requirements need to be installed before using the package. Please refer to > the [installation guide](https://rofunc.readthedocs.io/en/latest/installation.html) for more details. ### Install from Source (nightly version, recommended) ```shell git clone https://github.com/Skylark0924/Rofunc.git cd Rofunc # Create a conda environment # Python 3.8 is strongly recommended conda create -n rofunc python=3.8 # For Linux user sh ./scripts/install.sh # [Option] Install with baseline RL frameworks (SKRL, RLlib, Stable Baselines3) sh ./scripts/install_w_baselines.sh # [Option] For MacOS user (brew is required, Isaac Gym based simulator is not supported on MacOS) sh ./scripts/mac_install.sh ``` > **Note** > If you want to use functions related to ZED camera, you need to > install [ZED SDK](https://www.stereolabs.com/developers/release/#downloads) manually. (We have tried to package it as > a `.whl` file to add it to `requirements.txt`, unfortunately, the ZED SDK is not very friendly and doesn't support > direct installation.) ## Documentation [![Documentation](https://img.shields.io/badge/Documentation-Access-brightgreen?style=for-the-badge)](https://rofunc.readthedocs.io/en/latest/) [![Example Gallery](https://img.shields.io/badge/Example%20Gallery-Access-brightgreen?style=for-the-badge)](https://rofunc.readthedocs.io/en/latest/auto_examples/index.html) To give you a quick overview of the pipeline of `rofunc`, we provide an interesting example of learning to play Taichi from human demonstration. You can find it in the [Quick start](https://rofunc.readthedocs.io/en/latest/quickstart.html) section of the documentation. The available functions and plans can be found as follows. > **Note** > ✅: Achieved 🔃: Reformatting ⛔: TODO | Data | | Learning | | P&C | | Tools | | Simulator | | |:-------------------------------------------------------------------------------:|---|:--------------------------------------------------------------------------------------:|----|:-----------------------------------------------------------------------:|-----|:----------------:|----|:-------------------------------------------------------------------------:|----| | [`xsens.record`](https://rofunc.readthedocs.io/en/latest/devices/xsens.html) | ✅ | `DMP` | ⛔ | [`LQT`](https://rofunc.readthedocs.io/en/latest/planning/lqt.html) | ✅ | `Config` | ✅ | [`Franka`](https://rofunc.readthedocs.io/en/latest/simulator/franka.html) | ✅ | | [`xsens.export`](https://rofunc.readthedocs.io/en/latest/devices/xsens.html) | ✅ | `GMR` | ✅ | `LQTBi` | ✅ | `robolab.coord` | ✅ | [`CURI`](https://rofunc.readthedocs.io/en/latest/simulator/curi.html) | ✅ | | [`xsens.visual`](https://rofunc.readthedocs.io/en/latest/devices/xsens.html) | ✅ | `TPGMM` | ✅ | [`LQTFb`](https://rofunc.readthedocs.io/en/latest/planning/lqt_fb.html) | ✅ | `robolab.fk` | ✅ | `CURIMini` | 🔃 | | [`opti.record`](https://rofunc.readthedocs.io/en/latest/devices/optitrack.html) | ✅ | `TPGMMBi` | ✅ | [`LQTCP`](https://rofunc.readthedocs.io/en/latest/planning/lqt_cp.html) | ✅ | `robolab.ik` | ✅ | `CURISoftHand` | ✅ | | [`opti.export`](https://rofunc.readthedocs.io/en/latest/devices/optitrack.html) | ✅ | `TPGMM_RPCtl` | ✅ | `LQTCPDMP` | ✅ | `robolab.fd` | ⛔ | `Walker` | ✅ | | [`opti.visual`](https://rofunc.readthedocs.io/en/latest/devices/optitrack.html) | ✅ | `TPGMM_RPRepr` | ✅ | `LQR` | ✅ | `robolab.id` | ⛔ | `Gluon` | 🔃 | | [`zed.record`](https://rofunc.readthedocs.io/en/latest/devices/zed.html) | ✅ | `TPGMR` | ✅ | `PoGLQRBi` | ✅ | `visualab.dist` | ✅ | `Baxter` | 🔃 | | [`zed.export`](https://rofunc.readthedocs.io/en/latest/devices/zed.html) | ✅ | `TPGMRBi` | ✅ | [`iLQR`](https://rofunc.readthedocs.io/en/latest/planning/ilqr.html) | 🔃 | `visualab.ellip` | ✅ | `Sawyer` | 🔃 | | [`zed.visual`](https://rofunc.readthedocs.io/en/latest/devices/zed.html) | ✅ | `TPHSMM` | ✅ | `iLQRBi` | 🔃 | `visualab.traj` | ✅ | `Multi-Robot` | ✅ | | `emg.record` | ✅ | [`RLBaseLine(SKRL)`](https://rofunc.readthedocs.io/en/latest/lfd/RLBaseLine/SKRL.html) | ✅ | `iLQRFb` | 🔃 | | | | | | `emg.export` | ✅ | `RLBaseLine(RLlib)` | ✅ | `iLQRCP` | 🔃 | | | | | | `emg.visual` | ✅ | `RLBaseLine(ElegRL)` | ✅ | `iLQRDyna` | 🔃 | | | | | | `mmodal.record` | ⛔ | `BCO(RofuncIL)` | 🔃 | `iLQRObs` | 🔃 | | | | | | `mmodal.export` | ✅ | `BC-Z(RofuncIL)` | ⛔ | `MPC` | ⛔ | | | | | | | | `STrans(RofuncIL)` | ⛔ | `RMP` | ⛔ | | | | | | | | `RT-1(RofuncIL)` | ⛔ | | | | | | | | | | [`A2C(RofuncRL)`](https://rofunc.readthedocs.io/en/latest/lfd/RofuncRL/A2C.html) | ✅ | | | | | | | | | | [`PPO(RofuncRL)`](https://rofunc.readthedocs.io/en/latest/lfd/RofuncRL/PPO.html) | ✅ | | | | | | | | | | [`SAC(RofuncRL)`](https://rofunc.readthedocs.io/en/latest/lfd/RofuncRL/SAC.html) | ✅ | | | | | | | | | | [`TD3(RofuncRL)`](https://rofunc.readthedocs.io/en/latest/lfd/RofuncRL/TD3.html) | ✅ | | | | | | | | | | `CQL(RofuncRL)` | ⛔ | | | | | | | | | | `TD3BC(RofuncRL)` | ⛔ | | | | | | | | | | `DTrans(RofuncRL)` | 🔃 | | | | | | | | | | `EDAC(RofuncRL)` | ⛔ | | | | | | | | | | [`AMP(RofuncRL)`](https://rofunc.readthedocs.io/en/latest/lfd/RofuncRL/AMP.html) | ✅ | | | | | | | | | | [`ASE(RofuncRL)`](https://rofunc.readthedocs.io/en/latest/lfd/RofuncRL/ASE.html) | ✅ | | | | | | | | | | `ODTrans(RofuncRL)` | ⛔ | | | | | | | ## Star History [![Star History Chart](https://api.star-history.com/svg?repos=Skylark0924/Rofunc&type=Date)](https://star-history.com/#Skylark0924/Rofunc&Date) ## Citation If you use rofunc in a scientific publication, we would appreciate citations to the following paper: ``` @misc{Rofunc2022, author = {Liu, Junjia and Li, Chenzui and Delehelle, Donatien and Li, Zhihao and Chen, Fei}, title = {Rofunc: The full process python package for robot learning from demonstration and robot manipulation}, year = {2022}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/Skylark0924/Rofunc}}, } ``` ## Related Papers 1. Robot cooking with stir-fry: Bimanual non-prehensile manipulation of semi-fluid objects ([IEEE RA-L 2022](https://arxiv.org/abs/2205.05960) | [Code](rofunc/learning/RofuncIL/structured_transformer/strans.py)) ``` @article{liu2022robot, title={Robot cooking with stir-fry: Bimanual non-prehensile manipulation of semi-fluid objects}, author={Liu, Junjia and Chen, Yiting and Dong, Zhipeng and Wang, Shixiong and Calinon, Sylvain and Li, Miao and Chen, Fei}, journal={IEEE Robotics and Automation Letters}, volume={7}, number={2}, pages={5159--5166}, year={2022}, publisher={IEEE} } ``` 2. SoftGPT: Learn Goal-oriented Soft Object Manipulation Skills by Generative Pre-trained Heterogeneous Graph Transformer ([IROS 2023](https://arxiv.org/abs/2306.12677) ｜ Code coming soon) 3. Learning Robot Generalized Bimanual Coordination using Relative Parameterization Method on Human Demonstration (IEEE CDC 2023 | [Code](./rofunc/learning/ml/tpgmm.py)) ## The Team Rofunc is developed and maintained by the [CLOVER Lab (Collaborative and Versatile Robots Laboratory)](https://feichenlab.com/), CUHK. ## Acknowledge We would like to acknowledge the following projects: ### Learning from Demonstration 1. [pbdlib](https://gitlab.idiap.ch/rli/pbdlib-python) 2. [Ray RLlib](https://docs.ray.io/en/latest/rllib/index.html) 3. [ElegantRL](https://github.com/AI4Finance-Foundation/ElegantRL) 4. [SKRL](https://github.com/Toni-SM/skrl) ### Planning and Control 1. [Robotics codes from scratch (RCFS)](https://gitlab.idiap.ch/rli/robotics-codes-from-scratch)