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 Li, Chenzui and Delehelle, Donatien and Li, Zhihao and Chen, Fei},
          month=jun,
          year= 2023,
          publisher={Zenodo},
          doi={10.5281/zenodo.8084510},
          url={https://doi.org/10.5281/zenodo.8084510}
}

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.