What is EAGERx

You can use EAGERx (Engine Agnostic Graph Environments for Robotics) to easily define new (Gymnasium compatible) environments with modular robot definitions.

It enables users to:

• Define environments as graphs of nodes

• Visualize these graph environments interactively in a GUI

• Use a single graph environment both in reality and with various simulators

EAGERx explicitly addresses the differences in learning between simulation and reality, with native support for essential features such as:

• Safety layers and various other state, action and time-scale abstractions

• Delay simulation & domain randomization

• Real-world reset routines

• Synchronized parallel computation within a single environment

You can find the open-source code on Github.

Sim2Real: Policies trained in simulation and zero-shot evaluated on real systems using EAGERx. In the top left the successful transfer of a policy for the classic pendulum swing-up problem is shown and in the top right for a box-pushing task. Below that a policy to land a quadrotor on a moving inclined platform is shown.

Modular: The modular design of EAGERx allows users to create complex environments easily through composition.

GUI: Users can visualize their graph environment. Here we visualize the graph environment that we built in this tutorial. See the documentation for more information.

Applications beyond RL: The modular design and unified software pipeline of the framework have utility beyond reinforcement learning. We explored two such instances: interactive language-conditioned imitation learning (left) and classical control with deep learning based perception in a swimming pool environment (right).

Video

Engines

EAGERx enables a unified pipeline for real-world and simulated learning across various simulators. The following engines/simulators are already available for training and evaluation:

• RealEngine for real-world experiments

• PybulletEngine for PyBullet simulations

• OdeEngine for simulations based on ordinary differential equations (ODEs)

Users can easily add their own engines by implementing the Engine interface.

Table of Contents

• Getting Started
  • Installing EAGERx
  • Extras: GUI
  • Extras: training visualization
  • Other Dependencies
• Tutorials
  • Colabs
  • Visualizing your environment
  • Distributed
• API Reference
  • Engine
  • Backend
  • Processor
  • Engine State
  • Nodes
  • Object
  • Specs
  • Graph
  • Environment
  • Utilities
• Code Examples
• Troubleshooting
• Contributing to EAGERx
  • Creating a Package

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}
}

Maintainers

EAGERx is currently maintained by Bas van der Heijden (@bheijden) and Jelle Luijkx (@jelledouwe).

How to contact us

For any question, send an e-mail to eagerx.dev@gmail.com.

Acknowledgements

EAGERx is funded by the OpenDR Horizon 2020 project.