https://github.com/huggingface/gym-xarm
A gym environment for xArm
https://github.com/huggingface/gym-xarm
Last synced: 5 months ago
JSON representation
A gym environment for xArm
- Host: GitHub
- URL: https://github.com/huggingface/gym-xarm
- Owner: huggingface
- License: apache-2.0
- Created: 2024-03-30T11:44:29.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-04-28T12:31:58.000Z (11 months ago)
- Last Synced: 2025-10-12T15:05:46.442Z (5 months ago)
- Language: Python
- Homepage:
- Size: 2.16 MB
- Stars: 72
- Watchers: 7
- Forks: 18
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-deep-reinforcement-learning - huggingface/gym-xarm
README
# gym-xarm
A gym environment for xArm
## Installation
Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniconda`](https://docs.anaconda.com/free/miniconda/index.html):
```bash
conda create -y -n xarm python=3.10 && conda activate xarm
```
Install gym-xarm:
```bash
pip install gym-xarm
```
## Quickstart
```python
# example.py
import gymnasium as gym
import gym_xarm
env = gym.make("gym_xarm/XarmLift-v0", render_mode="human")
observation, info = env.reset()
for _ in range(1000):
action = env.action_space.sample()
observation, reward, terminated, truncated, info = env.step(action)
image = env.render()
if terminated or truncated:
observation, info = env.reset()
env.close()
```
To use this [example](./example.py) with `render_mode="human"`, you should set the environment variable `export MUJOCO_GL=glfw` or simply run
```bash
MUJOCO_GL=glfw python example.py
```
## Description for `Lift` task
The goal of the agent is to lift the block above a height threshold. The agent is an xArm robot arm and the block is a cube.
### Action Space
The action space is continuous and consists of four values [x, y, z, w]:
- [x, y, z] represent the position of the end effector
- [w] represents the gripper control
### Observation Space
Observation space is dependent on the value set to `obs_type`:
- `"state"`: observations contain agent and object state vectors only (no rendering)
- `"pixels"`: observations contains rendered image only (no state vectors)
- `"pixels_agent_pos"`: contains rendered image and agent state vector
## Contribute
Instead of using `pip` directly, we use `poetry` for development purposes to easily track our dependencies.
If you don't have it already, follow the [instructions](https://python-poetry.org/docs/#installation) to install it.
Install the project with dev dependencies:
```bash
poetry install --all-extras
```
### Follow our style
```bash
# install pre-commit hooks
pre-commit install
# apply style and linter checks on staged files
pre-commit
```
## Acknowledgment
gym-xarm is adapted from [FOWM](https://www.yunhaifeng.com/FOWM/) and is based on work by [Nicklas Hansen](https://nicklashansen.github.io/), [Yanjie Ze](https://yanjieze.com/), [Rishabh Jangir](https://jangirrishabh.github.io/), [Mohit Jain](https://natsu6767.github.io/), and [Sambaran Ghosal](https://github.com/SambaranRepo) as part of the following publications:
* [Self-Supervised Policy Adaptation During Deployment](https://arxiv.org/abs/2007.04309)
* [Generalization in Reinforcement Learning by Soft Data Augmentation](https://arxiv.org/abs/2011.13389)
* [Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation](https://arxiv.org/abs/2107.00644)
* [Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulation](https://arxiv.org/abs/2201.07779)
* [Visual Reinforcement Learning with Self-Supervised 3D Representations](https://arxiv.org/abs/2210.07241)