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).
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.
.. toctree:: :maxdepth: 2 :caption: Table of Contents guide/getting_started/index guide/tutorials/index guide/api_reference/index guide/examples/index guide/troubleshooting/index guide/contributing/index
If you are using EAGERx for your scientific publications, please cite:
@article{eagerx,
author = {van der Heijden, Bas and Luijkx, Jelle, and Ferranti, Laura and Kober, Jens and Babuska, Robert},
title = {EAGERx: Engine Agnostic Graph Environments for Robotics},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/eager-dev/eagerx}}
}EAGERx is currently maintained by Bas van der Heijden (@bheijden) and Jelle Luijkx (@jelledouwe).
For any question, send an e-mail to [email protected].
EAGERx is funded by the OpenDR Horizon 2020 project.
