rofunc 0.0.1.2

The Full Process Python Package for Robot Learning from Demonstration

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

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

Rofunc package focuses on the robotic Imitation Learning (IL) and Learning from Demonstration (LfD) fields and provides valuable and convenient python functions for robotics, 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.

Installation

Install from PyPI

The installation is very easy,

pip install rofunc

and as you'll find later, it's easy to use as well!

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 for more details.

Install from Source (Recommended)

git clone https://github.com/Skylark0924/Rofunc.git
cd Rofunc

# Create a conda environment
conda create -n rofunc python=3.8
conda activate rofunc

# Install the requirements and rofunc
pip install -r requirements.txt
pip install .

Note If you want to use functions related to ZED camera, you need to install ZED SDK 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.)

Star History

Documentation

Note Currently, we provide a simple document; please refer to here. A comprehensive one with both English and Chinese versions is built via the readthedoc. We provide a simple but interesting example: learning to play Taichi by learning from human demonstration.

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 section of the documentation.

The available functions and plans can be found as follows.

Classes	Types	Functions	Description	Status
Demonstration collection and pre-processing	Xsens	xsens.record	Record the human motion via network streaming	✅
		xsens.process	Decode the .mvnx file	✅
		xsens.visualize	Show or save gif about the motion	✅
	Optitrack	optitrack.record	Record the motion of markers via network streaming	✅
		optitrack.process	Process the output .csv data	✅
		optitrack.visualize	Show or save gif about the motion
	ZED	zed.record	Record with multiple (1~n) cameras	✅
		zed.playback	Playback the recording and save snapshots	✅
		zed.export	Export the recording to mp4 or image sequences	✅
	Delsys EMG	emg.record	Record real-time EMG data via network streaming	✅
		emg.process	Filtering the EMG data	✅
		emg.visualize	Some visualization functions for EMG data	✅
	Multimodal	mmodal.record	Record multi-modal demonstration data simultaneously
		mmodal.export	Export multi-modal demonstration data in one line	✅
Learning from Demonstration	Machine learning	dmp.uni	DMP for uni-manual robot with several (or one) demonstrated trajectories
		gmr.uni	GMR for uni-manual robot with several (or one) demonstrated trajectories	✅
		gmm.uni	GMM for uni-manual robot with several (or one) demonstrated trajectories
		tpgmm.uni	TP-GMM for uni-manual robot with several (or one) demonstrated trajectories	✅
		tpgmm.bi	TP-GMM for bimanual robot with coordination learned from demonstration	✅
		tpgmr.uni	TP-GMR for uni-manual robot with several (or one) demonstrated trajectories	✅
		tpgmr.bi	TP-GMR for bimanual robot with coordination learned from demonstration	✅
	Deep learning	bco	Behavior cloning from observation	✅
		strans	Structured-Transformer method proposed in IEEE RAL
	Reinforcement learning	SKRL (ppo, sac, tq3)	Provide API for SKRL framework and robot examples in Isaac Gym	✅
		StableBaseline3 (ppo, sac, tq3)	Provide API for StableBaseline3 framework and robot examples in Isaac Gym
		RLlib (ppo, sac, tq3)	Provide API for Ray RLlib framework and robot examples in Isaac Gym	✅
		ElegantRL (ppo, sac, tq3)	Provide API for ElegantRL framework and robot examples in Isaac Gym	✅
		cql	Conservative Q-learning for fully offline learning
Planning	LQT	lqt.uni	Linear Quadratic Tracking (LQT) for uni-manual robot with several via-points	✅
		lqt.bi	LQT for bimanual robot with coordination constraints	✅
		lqt.uni_fb	Generate smooth trajectories with feedback	✅
		lqt.uni_cp	LQT with control primitive	✅
	iLQR	ilqr.uni	Iterative Linear Quadratic Regulator (iLQR) for uni-manual robot with several via-points	✅
		ilqr.bi	iLQR for bimanual robots with several via-points	✅
		ilqr.uni_fb	iLQR with feedback
		ilqr.uni_cp	iLQR with control primitive	✅
		ilqr.uni_obstacle	iLQR with obstacle avoidance	✅
		ilqr.uni_dyna	iLQR with dynamics and force control	✅
	MPC	mpc.uni	Model Predictive Control (MPC)
	CIO	cio.whole	Contact-invariant Optimization (CIO)
Tools	Logger	logger.write	General logger based on tensorboard
	Config	config.get_config	General config API based on hydra	✅
	VisuaLab	visualab.trajectory	2-dim/3-dim/with ori trajectory visualization	✅
		visualab.distribution	2-dim/3-dim distribution visualization	✅
		visualab.ellipsoid	2-dim/3-dim ellipsoid visualization	✅
	RoboLab	robolab.transform	Useful functions about coordinate transformation	✅
		robolab.fk	Forward kinematics w.r.t URDF file	✅
		robolab.ik	Inverse kinematics w.r.t URDF file	✅
		robolab.fd	Forward dynamics w.r.t URDF file
		robolab.id	Inverse dynamics w.r.t URDF file
Simulator	Franka	franka.sim	Execute specific trajectory via single Franka Panda arm in Isaac Gym	✅
	CURI mini	curi_mini.sim	Execute specific trajectory via dual Franka Panda arm in Isaac Gym
	CURI	curi.sim	Execute specific trajectory via human-like CURI robot in Isaac Gym	✅
	Walker	walker.sim	Execute specific trajectory via UBTECH Walker robot in Isaac Gym	✅

Cite

If you use rofunc in a scientific publication, we would appreciate citations to the following paper:

@misc{Rofunc2022,
      author = {Liu, Junjia and Li, Zhihao and Li, Chenzui},
      title = {Rofunc: The full process python package for robot learning from demonstration},
      year = {2022},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\url{https://github.com/Skylark0924/Rofunc}},
}

The Team

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