https://github.com/dfki-ric/deformable_gym
A collection of RL gymnasium environments for learning to grasp 3D deformable objects.
https://github.com/dfki-ric/deformable_gym
deformable-object gym-environments gymnasium-environment mujoco pybullet reinforcement-learning robotic-manipulation robotics
Last synced: 5 months ago
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A collection of RL gymnasium environments for learning to grasp 3D deformable objects.
- Host: GitHub
- URL: https://github.com/dfki-ric/deformable_gym
- Owner: dfki-ric
- License: other
- Created: 2023-04-06T08:00:46.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2025-08-04T17:09:16.000Z (10 months ago)
- Last Synced: 2026-01-09T03:28:06.964Z (5 months ago)
- Topics: deformable-object, gym-environments, gymnasium-environment, mujoco, pybullet, reinforcement-learning, robotic-manipulation, robotics
- Language: Python
- Homepage:
- Size: 38 MB
- Stars: 46
- Watchers: 8
- Forks: 2
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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# DeformableGym
This repository contains a collection of [gymnasium](https://github.com/Farama-Foundation/Gymnasium) environments built with [PyBullet](https://pybullet.org/) and [MuJoCo](https://github.com/google-deepmind/mujoco).
In these environments, the agent needs to learn to grasp deformable 3D objects such as shoe insoles or pillows from sparse reward signals.
## Installation
### Install from PyPI
The easiest way to install deformable_gym is via PyPI:
```bash
pip install deformable_gym
```
### Install from source
Alternatively, e.g. if you want to develop extensions, you can also clone the
repository and then install it in editable mode:
```bash
git clone git@github.com:dfki-ric/deformable_gym.git
pip install -e .
```
### Optional dependencies
To install the mujoco versions of the environments, use `pip install 'deformable_gym[mujoco]'`.
To install the development dependencies, use `pip install 'deformable_gym[dev]'.
You can install all optional dependencies using `pip install 'deformable_gym[all]'.
## Available environments
| Environment Name | PyBullet | MuJoCo |
| ------------------------- | :----------------: | :----------------: |
| FloatingMiaGraspInsole | :heavy_check_mark: | :heavy_check_mark: |
| FloatingShadowGraspInsole | :heavy_check_mark: | :heavy_check_mark: |
| FloatingMiaGraspPillow | :heavy_check_mark: | :heavy_check_mark: |
| FloatingShadowGraspPillow | :heavy_check_mark: | :heavy_check_mark: |
| URMiaGraspInsole | :heavy_check_mark: | :heavy_check_mark: |
| URShadowGraspInsole | :heavy_check_mark: | :heavy_check_mark: |
| URMiaGraspPillow | :heavy_check_mark: | :heavy_check_mark: |
| URShadowGraspPillow | :heavy_check_mark: | :heavy_check_mark: |
## Example
Here is an example of how to run a random agent in the FloatingMiaEnv. More can be found in the `examples/` folder.
```Python
import gymnasium
"""
=========
Floating Mia Example
=========
This is an example of how to use the FloatingMiaGraspEnv. A random policy is then
used to generate ten episodes.
"""
env = gymnasium.make("FloatingMiaGraspInsole-v0")
env.reset()
episode_return = 0
num_episodes = 0
while num_episodes <= 10:
action = env.action_space.sample()
state, reward, terminated, truncated, _ = env.step(action)
episode_return += reward
if terminated or truncated:
print(f"Episode finished with return {episode_return}!")
num_episodes += 1
env.reset()
```
## Documentation
The documentation can be found in the directory doc. To build the documentation, run e.g. (on linux):
```bash
cd doc
make html
```
The HTML documentation is now located at doc/build/html/index.html. You need the following packages to build the documentation:
```bash
pip install numpydoc sphinx sphinx-gallery sphinx-bootstrap-theme
```
## Contributing
If you wish to report bugs, please use the [issue tracker](https://github.com/dfki-ric/deformable_gym/issues). If you would like to contribute to DeformableGym, just open an issue or a
[pull request](https://github.com/dfki-ric/deformable_gym/pulls). The target branch for
merge requests is the development branch. The development branch will be merged to main for new releases. If you have
questions about the software, you should ask them in the discussion section.
The recommended workflow to add a new feature, add documentation, or fix a bug is the following:
- Push your changes to a branch (e.g. feature/x, doc/y, or fix/z) of your fork of the deformable_gym repository.
- Open a pull request to the latest development branch. There is usually an open merge request from the latest development branch to the main branch.
- When the latest development branch is merged to the main branch, a new release will be made.
Note that there is a checklist for new features.
It is forbidden to directly push to the main branch. Each new version has its own development branch from which a pull request will be opened to the main branch. Only the maintainers of the software are allowed to merge a development branch to the main branch.
## Referencing
If you use DeformableGym in your research, please consider citing it. You may find the paper [here](https://deformable-workshop.github.io/icra2023/spotlight/03-Laux-spotlight.pdf).
```
@inproceedings{Laux2023,
title = {Grasping 3D Deformable Objects via Reinforcement Learning: A Benchmark and Evaluation},
booktitle = {3rd Workshop on Representing and Manipulating Deformable Objects @ ICRA2023},
author = {Melvin Laux and Chandandeep Singh and Alexander Fabisch},
month = {May},
year = {2023},
url = {https://deformable-workshop.github.io/icra2023/},
}
```
## Releases
### Semantic Versioning
[Semantic versioning](https://semver.org/) must be used, that is, the major version number will be
incremented when the API changes in a backwards incompatible way, the minor
version will be incremented when new functionality is added in a backwards
compatible manner, and the patch version is incremented for bugfixes,
documentation, etc.
## Funding
This library has been developed initially at the
[Robotics Innovation Center](http://robotik.dfki-bremen.de/en/startpage.html) of the
[German Research Center for Artificial Intelligence (DFKI)](http://www.dfki.de) in Bremen together with the
[Robotics Group](https://robotik.dfki-bremen.de/en/about-us/university-of-bremen-robotics-group.html) of the
[University of Bremen](http://www.uni-bremen.de/en.html). At this phase, the work was supported through a grant from the European
Commission (870142).
