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MyoSuite-1.2.3
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Musculoskeletal environments simulated in MuJoCo
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | MyoSuite-1.2.3 |
| نام | MyoSuite |
| نسخه کتابخانه | 1.2.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | MyoSuite Authors - Vikash Kumar (Meta AI), Vittorio Caggiano (Meta AI), Huawei Wang (University of Twente), Guillaume Durandau (University of Twente), Massimo Sartori (University of Twente) |
| ایمیل نویسنده | vikashplus@gmail.com |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/MyoSuite/ |
| مجوز | Apache 2.0 |
<!-- =================================================
# Copyright (c) Facebook, Inc. and its affiliates
Authors :: Vikash Kumar (vikashplus@gmail.com), Vittorio Caggiano (caggiano@gmail.com)
================================================= -->
<img src="https://github.com/facebookresearch/myosuite/blob/main/docs/source/images/Full%20Color%20Horizontal%20wider.png?raw=true" width=800>
[](https://github.com/ET-BE/myoChallengeEval) [](https://opensource.facebook.com/support-ukraine)
[](https://myosuite.readthedocs.io/en/latest/)
[](https://github.com/facebookresearch/myosuite/blob/main/docs/CONTRIBUTING.md)
[](https://pepy.tech/project/myosuite)
[](https://colab.research.google.com/drive/1U6vo6Q_rPhDaq6oUMV7EAZRm6s0fD1wn?usp=sharing)
`MyoSuite` is a collection of musculoskeletal environments and tasks simulated with the [MuJoCo](http://www.mujoco.org/) physics engine and wrapped in the OpenAI ``gym`` API to enable the application of Machine Learning to bio-mechanic control problems.
[Full task details](https://github.com/facebookresearch/myosuite/blob/main/docs/source/suite.rst#tasks) | [Baselines](https://github.com/facebookresearch/myosuite/tree/main/myosuite/agents/baslines_NPG) | [Documentation](https://myosuite.readthedocs.io/en/latest/)
| [Tutorials](https://github.com/facebookresearch/myosuite/tree/main/docs/source/tutorials)
| 🔥🆕 [NeurIPS-MyoChallenge 2022](https://sites.google.com/view/myochallenge)
Below is an overview of the tasks in the MyoSuite.
<img width="1240" alt="TasksALL" src="https://github.com/facebookresearch/myosuite/blob/main/docs/source/images/myoSuite_All.png?raw=true">
## Getting Started
You will need Python 3.7.1 or later versions. At this moment, the library has been tested **only on MacOs and Linux** with [MuJoCo v2.1.0](https://github.com/deepmind/mujoco/releases/tag/2.1.0).
It is recommended to use [Miniconda](https://docs.conda.io/en/latest/miniconda.html#latest-miniconda-installer-links) and to create a separate environment with:
``` bash
conda create --name myosuite python=3.7.1
conda activate myosuite
```
It is possible to install MyoSuite with:
``` bash
pip install -U myosuite
```
for advanced installation, see [here](setup/README.md).
Test your installation using the following command (this will return also a list of all the current environments):
``` bash
python myosuite/tests/test_myo.py
```
You can also visualize the environments with random controls using the command below:
```
$ python myosuite/utils/examine_env.py --env_name myoElbowPose1D6MRandom-v0
```
**NOTE:** If the visualization results in a GLFW error, this is because `mujoco-py` does not see some graphics drivers correctly. This can usually be fixed by explicitly loading the correct drivers before running the python script. See [this page](setup/README.md#known-issues) for details.
## Examples
It is possible to create and interface with MyoSuite environments just like any other OpenAI gym environments. For example, to use the `myoElbowPose1D6MRandom-v0` environment, it is possible simply to run: [](https://colab.research.google.com/drive/1U6vo6Q_rPhDaq6oUMV7EAZRm6s0fD1wn?usp=sharing)
```python
import myosuite
import gym
env = gym.make('myoElbowPose1D6MRandom-v0')
env.reset()
for _ in range(1000):
env.sim.render(mode='window')
env.step(env.action_space.sample()) # take a random action
env.close()
```
You can find [tutorials](https://github.com/facebookresearch/myosuite/tree/main/docs/source/tutorials#tutorials) on how to load MyoSuite models/tasks, train them, and visualize their outcome. Also, you can find [baselines](https://github.com/facebookresearch/myosuite/tree/main/myosuite/agents) to test some pre-trained policies.
## License
MyoSuite is licensed under the [Apache License](LICENSE).
## Citation
If you find this repository useful in your research, please consider giving a star ⭐ and cite our [arXiv paper](https://arxiv.org/abs/2205.13600) by using the following BibTeX entrys.
```BibTeX
@Misc{MyoSuite2022,
author = {Vittorio, Caggiano AND Huawei, Wang AND Guillaume, Durandau AND Massimo, Sartori AND Vikash, Kumar},
title = {MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control},
publisher = {arXiv},
year = {2022},
howpublished = {\url{https://github.com/facebookresearch/myosuite}},
year = {2022}
doi = {10.48550/ARXIV.2205.13600},
url = {https://arxiv.org/abs/2205.13600},
}
```
نیازمندی
| مقدار | نام |
|---|---|
| - | click |
| - | sk-video |
| ==0.13 | gym |
| - | free-mujoco-py |
| - | termcolor |
| - | flatten-dict |
| ==3.7.0 | h5py |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7.1 | Python |
نحوه نصب
نصب پکیج whl MyoSuite-1.2.3:
pip install MyoSuite-1.2.3.whl
نصب پکیج tar.gz MyoSuite-1.2.3:
pip install MyoSuite-1.2.3.tar.gz