Generic URDF robots
===================
In this package, generic urdf robots and a panda gym environment are
available. The goal is to make this environment as easy as possible to
deploy. Although, we used the OpenAI-Gym framing, these environments are
not necessarly restricted to Reinforcement-Learning but rather to local
motion planning in general.
Pybullet
--------
<table>
<tr>
<td> Point Robot </td>
<td> Point Robot with Keyboard Input </td>
<td> Non-Holonomic Robot </td>
</tr>
<tr>
<td> <img src="/docs/source/img/pointRobot.gif" width="250" height="250"/> </td>
<td> <img src="/docs/source/img/pointRobotKeyboardInput.gif" width="250" height="250"/> </td>
<td> <img src="/docs/source/img/boxerRobot.gif" width="250" height="250"/> </td>
</tr>
</table>
<table>
<tr>
<td> Tiago Robot </td>
<td> Tiago Robot with Keyboard Input </td>
</tr>
<tr>
<td> <img src="/docs/source/img/tiago.gif" width="250" height="250"/> </td>
<td> <img src="/docs/source/img/tiagoKeyboardInput.gif" width="250" height="250"/> </td>
</tr>
</table>
<table>
<tr>
<td> Panda Robot </td>
<td> Albert Robot </td>
</tr>
<tr>
<td> <img src="/docs/source/img/panda.gif" width="250" height="250"/> </td>
<td> <img src="/docs/source/img/albert.gif" width="250" height="250"/> </td>
</tr>
</table>
Mujoco
--------
<table>
<tr>
<td> Point Robot </td>
<td> Panda Robot </td>
</tr>
<tr>
<td> <img src="/docs/source/img/pointRobot_mujoco.gif" width="250" height="250"/> </td>
<td> <img src="/docs/source/img/panda_without_gripper.gif" width="250" height="250"/> </td>
</tr>
</table>
Getting started
===============
This is the guide to quickle get going with urdf gym environments.
Pre-requisites
--------------
- Python \>=3.8
- pip3
- git
Installation from pypi
----------------------
The package is uploaded to pypi so you can install it using
``` {.sourceCode .bash}
pip3 install urdfenvs
```
Installation from source
------------------------
You first have to download the repository
``` {.sourceCode .bash}
git clone git@github.com:maxspahn/gym_envs_urdf.git
```
Then, you can install the package using pip as:
``` {.sourceCode .bash}
pip3 install .
```
The code can be installed in editible mode using
``` {.sourceCode .bash}
pip3 install -e .
```
Note that we recommend using poetry in this case.
Optional: Installation with poetry
----------------------------------
If you want to use [poetry](https://python-poetry.org/docs/), you have
to install it first. See their webpage for instructions
[docs](https://python-poetry.org/docs/). Once poetry is installed, you
can install the virtual environment with the following commands. Note
that during the first installation `poetry update` takes up to 300 secs.
``` {.sourceCode .bash}
poetry install
```
The virtual environment is entered by
``` {.sourceCode .bash}
poetry shell
```
Inside the virtual environment you can access all the examples.
Installing dependencies
-----------------------
Dependencies should be installed through pip or poetry, see below.
Using pip, you can use
``` {.sourceCode .bash}
pip3 install '.[options]'
```
Using poetry
``` {.sourceCode .bash}
poetry install --with <options>
```
Options are `keyboard`.
Examples
--------
You find several python scripts in
[examples/](https://github.com/maxspahn/gym_envs_urdf/tree/master/examples).
You can test those examples using the following (if you use poetry, make
sure to enter the virtual environment first with `poetry shell`)
``` {.sourceCode .python}
python3 pointRobot.py
```
Replace pointRobot.py with the name of the script you want to run.
Raw data
{
"_id": null,
"home_page": "https://maxspahn.github.io/gym_envs_urdf/",
"name": "urdfenvs",
"maintainer": "Max Spahn",
"docs_url": null,
"requires_python": "<3.11,>=3.8",
"maintainer_email": "m.spahn@tudelft.nl",
"keywords": "robotics, motion-planning, simulation",
"author": "Max Spahn",
"author_email": "m.spahn@tudelft.nl",
"download_url": "https://files.pythonhosted.org/packages/4b/99/75f07a40e55261565f97356e0bd496a6f8a3a29bc89560fafc0f7cd6f498/urdfenvs-0.9.15.tar.gz",
"platform": null,
"description": "Generic URDF robots\n===================\n\nIn this package, generic urdf robots and a panda gym environment are\navailable. The goal is to make this environment as easy as possible to\ndeploy. Although, we used the OpenAI-Gym framing, these environments are\nnot necessarly restricted to Reinforcement-Learning but rather to local\nmotion planning in general.\n\nPybullet\n--------\n\n<table>\n <tr>\n <td> Point Robot </td>\n <td> Point Robot with Keyboard Input </td>\n <td> Non-Holonomic Robot </td>\n </tr>\n <tr>\n <td> <img src=\"/docs/source/img/pointRobot.gif\" width=\"250\" height=\"250\"/> </td>\n <td> <img src=\"/docs/source/img/pointRobotKeyboardInput.gif\" width=\"250\" height=\"250\"/> </td> \n <td> <img src=\"/docs/source/img/boxerRobot.gif\" width=\"250\" height=\"250\"/> </td>\n </tr>\n</table>\n\n<table>\n <tr>\n <td> Tiago Robot </td>\n <td> Tiago Robot with Keyboard Input </td>\n </tr>\n <tr>\n <td> <img src=\"/docs/source/img/tiago.gif\" width=\"250\" height=\"250\"/> </td>\n <td> <img src=\"/docs/source/img/tiagoKeyboardInput.gif\" width=\"250\" height=\"250\"/> </td>\n </tr>\n</table>\n\n<table>\n <tr>\n <td> Panda Robot </td>\n <td> Albert Robot </td>\n </tr>\n <tr>\n <td> <img src=\"/docs/source/img/panda.gif\" width=\"250\" height=\"250\"/> </td>\n <td> <img src=\"/docs/source/img/albert.gif\" width=\"250\" height=\"250\"/> </td>\n </tr>\n</table>\n\nMujoco\n--------\n\n<table>\n <tr>\n <td> Point Robot </td>\n <td> Panda Robot </td>\n </tr>\n <tr>\n <td> <img src=\"/docs/source/img/pointRobot_mujoco.gif\" width=\"250\" height=\"250\"/> </td>\n <td> <img src=\"/docs/source/img/panda_without_gripper.gif\" width=\"250\" height=\"250\"/> </td>\n </tr>\n</table>\n\nGetting started\n===============\n\nThis is the guide to quickle get going with urdf gym environments.\n\nPre-requisites\n--------------\n\n- Python \\>=3.8\n- pip3\n- git\n\nInstallation from pypi\n----------------------\n\nThe package is uploaded to pypi so you can install it using\n\n``` {.sourceCode .bash}\npip3 install urdfenvs\n```\n\nInstallation from source\n------------------------\n\nYou first have to download the repository\n\n``` {.sourceCode .bash}\ngit clone git@github.com:maxspahn/gym_envs_urdf.git\n```\n\nThen, you can install the package using pip as:\n\n``` {.sourceCode .bash}\npip3 install .\n```\n\nThe code can be installed in editible mode using\n\n``` {.sourceCode .bash}\npip3 install -e .\n```\n\nNote that we recommend using poetry in this case.\n\nOptional: Installation with poetry\n----------------------------------\n\nIf you want to use [poetry](https://python-poetry.org/docs/), you have\nto install it first. See their webpage for instructions\n[docs](https://python-poetry.org/docs/). Once poetry is installed, you\ncan install the virtual environment with the following commands. Note\nthat during the first installation `poetry update` takes up to 300 secs.\n\n``` {.sourceCode .bash}\npoetry install\n```\n\nThe virtual environment is entered by\n\n``` {.sourceCode .bash}\npoetry shell\n```\n\nInside the virtual environment you can access all the examples.\n\nInstalling dependencies\n-----------------------\n\nDependencies should be installed through pip or poetry, see below.\n\nUsing pip, you can use\n\n``` {.sourceCode .bash}\npip3 install '.[options]'\n```\n\nUsing poetry\n\n``` {.sourceCode .bash}\npoetry install --with <options>\n```\n\nOptions are `keyboard`.\n\nExamples\n--------\n\nYou find several python scripts in\n[examples/](https://github.com/maxspahn/gym_envs_urdf/tree/master/examples).\nYou can test those examples using the following (if you use poetry, make\nsure to enter the virtual environment first with `poetry shell`)\n\n``` {.sourceCode .python}\npython3 pointRobot.py\n```\n\nReplace pointRobot.py with the name of the script you want to run.\n",
"bugtrack_url": null,
"license": "GPL-3.0-or-later",
"summary": "Simple simulation environment for robots, based on the urdf files.",
"version": "0.9.15",
"project_urls": {
"Documentation": "https://maxspahn.github.io/gym_envs_urdf/",
"Homepage": "https://maxspahn.github.io/gym_envs_urdf/",
"Repository": "https://github.com/maxspahn/gym_envs_urdf"
},
"split_keywords": [
"robotics",
" motion-planning",
" simulation"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "064d7044b23edc04da270cf845b142dd836ab84f47111c1825d8b2a856121a31",
"md5": "b191243f7c9bff17c2952a4e4d8bc7c5",
"sha256": "e6c58b003e9042a6bc320eb7e266c402e18150c4b2d4b02410ad8a6346aa1753"
},
"downloads": -1,
"filename": "urdfenvs-0.9.15-py3-none-any.whl",
"has_sig": false,
"md5_digest": "b191243f7c9bff17c2952a4e4d8bc7c5",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<3.11,>=3.8",
"size": 173791,
"upload_time": "2025-01-01T01:26:01",
"upload_time_iso_8601": "2025-01-01T01:26:01.778412Z",
"url": "https://files.pythonhosted.org/packages/06/4d/7044b23edc04da270cf845b142dd836ab84f47111c1825d8b2a856121a31/urdfenvs-0.9.15-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "4b9975f07a40e55261565f97356e0bd496a6f8a3a29bc89560fafc0f7cd6f498",
"md5": "157a9e571fc7f2bfc041a869481fdd31",
"sha256": "0b4eb60b87a4b22f1b663207ffa204bad8d921eb8b6749276589968562eaf35c"
},
"downloads": -1,
"filename": "urdfenvs-0.9.15.tar.gz",
"has_sig": false,
"md5_digest": "157a9e571fc7f2bfc041a869481fdd31",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<3.11,>=3.8",
"size": 155859,
"upload_time": "2025-01-01T01:26:06",
"upload_time_iso_8601": "2025-01-01T01:26:06.139667Z",
"url": "https://files.pythonhosted.org/packages/4b/99/75f07a40e55261565f97356e0bd496a6f8a3a29bc89560fafc0f7cd6f498/urdfenvs-0.9.15.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-01-01 01:26:06",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "maxspahn",
"github_project": "gym_envs_urdf",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "urdfenvs"
}
JSON
