https://github.com/robotology/gym-ignition

Framework for developing OpenAI Gym robotics environments simulated with Ignition Gazebo
https://github.com/robotology/gym-ignition

gazebo gym gym-ignition ignition ignition-gazebo ignitionrobotics openai openai-gym openai-gym-environment reinforcement-learning robotics scenario simulation

Last synced: about 1 month ago
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Framework for developing OpenAI Gym robotics environments simulated with Ignition Gazebo

• Host: GitHub
• URL: https://github.com/robotology/gym-ignition
• Owner: robotology
• License: lgpl-3.0
• Created: 2018-11-29T16:19:10.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2024-01-04T17:16:50.000Z (11 months ago)
• Last Synced: 2024-10-01T09:12:55.588Z (2 months ago)
• Topics: gazebo, gym, gym-ignition, ignition, ignition-gazebo, ignitionrobotics, openai, openai-gym, openai-gym-environment, reinforcement-learning, robotics, scenario, simulation
• Language: C++
• Homepage: https://robotology.github.io/gym-ignition
• Size: 61.4 MB
• Stars: 227
• Watchers: 18
• Forks: 26
• Open Issues: 45
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Codeowners: .github/CODEOWNERS

Awesome Lists containing this project

• awesome-deep-rl - gym-ignition - Experimental OpenAI Gym environments implemented with Ignition Robotics. (Environments)
• StarryDivineSky - robotology/gym-ignition

README

        


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> ⚠️ **Warning** ⚠️

>

> This project is no longer actively maintained, and development has stalled.

> For an in-depth description of the current status and actionable steps to revive development, please consult [robotology/gym-ignition#430]([url](https://github.com/robotology/gym-ignition/issues/430)).

||||

|:---:|:---:|:---:|

| ![][pendulum] | ![][panda] | ![][icub] |

[icub]: https://user-images.githubusercontent.com/469199/99262746-9e021a80-281e-11eb-9df1-d70134b0801a.png

[panda]: https://user-images.githubusercontent.com/469199/99263111-0cdf7380-281f-11eb-9cfe-338b2aae0503.png

[pendulum]: https://user-images.githubusercontent.com/469199/99262383-321fb200-281e-11eb-89cc-cc31f590daa3.png

## Description

**gym-ignition** is a framework to create **reproducible robotics environments** for reinforcement learning research.

It is based on the [ScenarIO](scenario/) project which provides the low-level APIs to interface with the Ignition Gazebo simulator.

By default, RL environments share a lot of boilerplate code, e.g. for initializing the simulator or structuring the classes

to expose the `gym.Env` interface.

Gym-ignition provides the [`Task`](python/gym_ignition/base/task.py) and [`Runtime`](python/gym_ignition/base/runtime.py)

abstractions that help you focusing on the development of the decision-making logic rather than engineering.

It includes [randomizers](python/gym_ignition/randomizers) to simplify the implementation of domain randomization

of models, physics, and tasks.

Gym-ignition also provides powerful dynamics algorithms compatible with both fixed-base and floating-based robots by

exploiting [robotology/idyntree](https://github.com/robotology/idyntree/) and exposing

[high-level functionalities](python/gym_ignition/rbd/idyntree).

Gym-ignition does not provide out-of-the-box environments ready to be used.

Rather, its aim is simplifying and streamlining their development.

Nonetheless, for illustrative purpose, it includes canonical examples in the

[`gym_ignition_environments`](python/gym_ignition_environments) package.

Visit the [website][website] for more information about the project.

[website]: https://robotology.github.io/gym-ignition

## Installation

1. First, follow the installation instructions of [ScenarIO](scenario/).

2. `pip install gym-ignition`, preferably in a [virtual environment](https://docs.python.org/3.8/tutorial/venv.html).

## Contributing

You can visit our community forum hosted in [GitHub Discussions](https://github.com/robotology/gym-ignition/discussions).

Even without coding skills, replying user's questions is a great way of contributing.

If you use gym-ignition in your application and want to show it off, visit the

[Show and tell](https://github.com/robotology/gym-ignition/discussions/categories/show-and-tell) section!

You can advertise there your environments created with gym-ignition.

Pull requests are welcome.

For major changes, please open a [discussion](https://github.com/robotology/gym-ignition/discussions)

first to propose what you would like to change.

## Citation

```bibtex

@INPROCEEDINGS{ferigo2020gymignition,

    title={Gym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning},

    author={D. {Ferigo} and S. {Traversaro} and G. {Metta} and D. {Pucci}},

    booktitle={2020 IEEE/SICE International Symposium on System Integration (SII)},

    year={2020},

    pages={885-890},

    doi={10.1109/SII46433.2020.9025951}

} 

```

## License

[LGPL v2.1](https://choosealicense.com/licenses/lgpl-2.1/) or any later version.

---

**Disclaimer:** Gym-ignition is an independent project and is not related by any means to OpenAI and Open Robotics.