Rofunc: The Full Process Python Package for Robot Learning from Demonstration and Robot Manipulation

Repository address: https://github.com/Skylark0924/Rofunc
Documentation: 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 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.

1.  Installation

Please refer to Installation for installation.

2.  Available functions and future plans

The available functions and plans can be found as follows.

Note ✅: Achieved 🔃: Reformatting ⛔: TODO

Data		Learning		P&C		Tools		Simulator
xsens.record	✅	DMP	⛔	LQT	✅	config	✅	Franka	✅
xsens.export	✅	GMR	✅	LQTBi	✅	logger	✅	CURI	✅
xsens.visual	✅	TPGMM	✅	LQTFb	✅	datalab	✅	CURIMini	🔃
opti.record	✅	TPGMMBi	✅	LQTCP	✅	robolab.coord	✅	CURISoftHand	✅
opti.export	✅	TPGMM_RPCtl	✅	LQTCPDMP	✅	robolab.fk	✅	Walker	✅
opti.visual	✅	TPGMM_RPRepr	✅	LQR	✅	robolab.ik	✅	Gluon	🔃
zed.record	✅	TPGMR	✅	PoGLQRBi	✅	robolab.fd	⛔	Baxter	🔃
zed.export	✅	TPGMRBi	✅	iLQR	🔃	robolab.id	⛔	Sawyer	🔃
zed.visual	✅	TPHSMM	✅	iLQRBi	🔃	visualab.dist	✅	Humanoid	✅
emg.record	✅	RLBaseLine(SKRL)	✅	iLQRFb	🔃	visualab.ellip	✅	Multi-Robot	✅
emg.export	✅	RLBaseLine(RLlib)	✅	iLQRCP	🔃	visualab.traj	✅
mmodal.record	⛔	RLBaseLine(ElegRL)	✅	iLQRDyna	🔃	oslab.dir_proc	✅
mmodal.sync	✅	BCO(RofuncIL)	🔃	iLQRObs	🔃	oslab.file_proc	✅
		BC-Z(RofuncIL)	⛔	MPC	⛔	oslab.internet	✅
		STrans(RofuncIL)	⛔	RMP	⛔	oslab.path	✅
		RT-1(RofuncIL)	⛔
		A2C(RofuncRL)	✅
		PPO(RofuncRL)	✅
		SAC(RofuncRL)	✅
		TD3(RofuncRL)	✅
		CQL(RofuncRL)	⛔
		TD3BC(RofuncRL)	⛔
		DTrans(RofuncRL)	✅
		EDAC(RofuncRL)	⛔
		AMP(RofuncRL)	✅
		ASE(RofuncRL)	✅
		ODTrans(RofuncRL)	⛔

3.  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。

4.  Related Papers

1. Robot cooking with stir-fry: Bimanual non-prehensile manipulation of semi-fluid objects (IEEE RA-L 2022 | Code)

@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｜Code coming soon)

@inproceedings{liu2023softgpt,
               title={Softgpt: Learn goal-oriented soft object manipulation skills by generative pre-trained heterogeneous graph transformer},
               author={Liu, Junjia and Li, Zhihao and Lin, Wanyu and Calinon, Sylvain and Tan, Kay Chen and Chen, Fei},
               booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
               pages={4920--4925},
               year={2023},
               organization={IEEE}
}

3. BiRP: Learning Robot Generalized Bimanual Coordination using Relative Parameterization Method on Human Demonstration (IEEE CDC 2023 | Code)

@article{liu2023birp,
        title={BiRP: Learning Robot Generalized Bimanual Coordination using Relative Parameterization Method on Human Demonstration},
        author={Liu, Junjia and Sim, Hengyi and Li, Chenzui and Chen, Fei},
        journal={arXiv preprint arXiv:2307.05933},
        year={2023}
}

5.  The Team

Rofunc is developed and maintained by the CLOVER Lab (Collaborative and Versatile Robots Laboratory), CUHK.