EAGERx Tutorials

Table of Contents

• What is the eagerx_tutorials package?
• Tutorials
• How to run the tutorials locally?
• Cite EAGERx
• Acknowledgements

What is the eagerx_tutorials package?

This repository/package contains Jupyter Notebooks with examples on how to use EAGERx. EAGERx (Engine Agnostic Graph Environments for Robotics) enables users to easily define new tasks, switch from one sensor to another, and switch from simulation to reality with a single line of code by being invariant to the physics engine.

The core repository is available here.

Full documentation and tutorials (including package creation and contributing) are available here.

Tutorials

The following tutorials are currently available.

Introduction to EAGERx

• Tutorial 1: Getting started
• Tutorial 2: Advanced usage

The solutions are available in here.

Developer tutorials

• Tutorial 1: Environment Creation and Training with EAGERx
• Tutorial 2: Reset and Step
• Tutorial 3: Space and Processors
• Tutorial 4: Nodes and Graph Validity
• Tutorial 5: Adding Engine Support for an Object
• Tutorial 6: Defining a new Object
• Tutorial 7: More Informative Rendering
• Tutorial 8: Reset Routines

The solutions are available in here.

How to run the tutorials locally?

As an alternative to running the tutorials in Google Colab, they can also be run locally in order to speed up computations.

Clone this repository and go to its root:

git clone git@github.com:eager-dev/eagerx_tutorials.git
cd eagerx_tutorials

Optional Create and source a virtual environment, (if venv is not installed run python3 -m pip install --user virtualenv):

python3 -m venv tutorial_env
source tutorial_env/bin/activate

Install the eagerx_tutorials package:

pip3 install -e .

Start Jupyter Lab:

jupyter lab

You will find the tutorials in the tutorials directory.

Cite EAGERx

If you are using EAGERx for your scientific publications, please cite:

@article{vanderheijden2024eagerx,
  author={van der Heijden, Bas and Luijkx, Jelle and Ferranti, Laura and Kober, Jens and Babuska, Robert},
  journal={IEEE Robotics \& Automation Magazine},
  title={Engine Agnostic Graph Environments for Robotics (EAGERx): A Graph-Based Framework for Sim2real Robot Learning},
  year={2024},
  volume={},
  number={},
  pages={2-15},
  keywords={Robots;Engines;Robot sensing systems;Delays;Robot learning;Physics;Codes},
  doi={10.1109/MRA.2024.3433172}
}

Acknowledgements

EAGERx is funded by the OpenDR Horizon 2020 project.