معرفی شرکت ها
dexterity-0.0.9
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
Software and tasks for dexterous multi-fingered hand manipulation, powered by MuJoCo
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dexterity-0.0.9 |
| نام | dexterity |
| نسخه کتابخانه | 0.0.9 |
| نگهدارنده | ['Kevin Zakka'] |
| ایمیل نگهدارنده | ['kevinarmandzakka@gmail.com'] |
| نویسنده | Kevin Zakka |
| ایمیل نویسنده | kevinarmandzakka@gmail.com |
| آدرس صفحه اصلی | https://github.com/kevinzakka/dexterity |
| آدرس اینترنتی | https://pypi.org/project/dexterity/ |
| مجوز | MIT |
# The MuJoCo Dexterity Suite (alpha-release)
[![PyPI Python Version][pypi-versions-badge]][pypi]
[![PyPI version][pypi-badge]][pypi]
[![dexterity-tests][tests-badge]][tests]
[pypi-versions-badge]: https://img.shields.io/pypi/pyversions/dexterity
[pypi-badge]: https://badge.fury.io/py/dexterity.svg
[pypi]: https://pypi.org/project/dexterity/
[tests-badge]: https://github.com/kevinzakka/dexterity/actions/workflows/build.yml/badge.svg
[tests]: https://github.com/kevinzakka/dexterity/actions/workflows/build.yml
Software and tasks for dexterous multi-fingered hand manipulation, powered by [MuJoCo](https://mujoco.org/).
<p float="left">
<img src="https://raw.githubusercontent.com/kevinzakka/dexterity/main/assets/reach.png" height="200">
<img src="https://raw.githubusercontent.com/kevinzakka/dexterity/main/assets/cube.png" height="200">
</p>
`dexterity` builds on [dm_control](https://github.com/deepmind/dm_control) and provides a collection of modular components that can be used to define rich Reinforcement Learning environments for dexterous manipulation. It also comes with a set of standardized tasks that can serve as a performance benchmark for the research community.
An introductory tutorial is available as a Colab notebook: [](https://colab.research.google.com/github/kevinzakka/dexterity/blob/main/tutorial.ipynb)
## Installation
### PyPI (Recommended)
The recommended way to install this package is via [PyPI](https://pypi.org/project/dexterity/):
```bash
pip install dexterity
```
### Source
We provide a [Miniconda](https://docs.conda.io/en/latest/miniconda.html) environment with Python 3.8 for development. To create it and install dependencies, run the following steps:
```bash
git clone https://github.com/kevinzakka/dexterity
cd dexterity
conda env create -f environment.yml # Creates a dexterity env.
conda activate dexterity
pip install .
```
## Overview
The MuJoCo `dexterity` suite is composed of the following core components:
* [`models`](dexterity/models/): MuJoCo models for dexterous hands and [PyMJCF](https://github.com/deepmind/dm_control/blob/main/dm_control/mjcf/README.md) classes for dynamically customizing them.
* [`inverse_kinematics`](dexterity/inverse_kinematics/): Inverse kinematics library for multi-fingered hands.
* [`effectors`](dexterity/effectors/): Interfaces for controlling hands and defining action spaces.
These components, in conjunction with `dm_control`, allow you to define and customize rich environments for reinforcement learning. We facilitate this process by providing the following:
* [`task`](dexterity/task.py): Wrappers over `composer.Task` that simplify the creation of generic dexterous tasks as well as goal-reaching based tasks (e.g., successive object reorientation).
* [`manipulation`](dexterity/manipulation/): A library of pre-defined, benchmark RL environments geared towards dexterous manipulation. For an overview of the available tasks, see the [task library](dexterity/manipulation/README.md).
Our hope is to grow the benchmark over time with crowd-sourced contributions from the research community -- PR contributions are welcome!
## Acknowledgements
A large part of the design and implementation of `dexterity` is inspired by the [MoMa](https://github.com/deepmind/dm_robotics/tree/main/py/moma) library in [dm_robotics](https://github.com/deepmind/dm_robotics/).
نیازمندی
| مقدار | نام |
|---|---|
| - | absl-py |
| - | numpy |
| - | typing-extensions |
| - | mujoco |
| >=1.0.1 | dm-control |
| - | dm-robotics-geometry |
| - | dm-robotics-transformations |
| - | black |
| - | isort |
| - | flake8 |
| - | mypy |
| - | ipdb |
| - | jupyter |
| - | pytest-xdist |
| - | matplotlib |
| - | imageio |
| - | imageio-ffmpeg |
| - | matplotlib |
| - | imageio |
| - | imageio-ffmpeg |
| - | pytest-xdist |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl dexterity-0.0.9:
pip install dexterity-0.0.9.whl
نصب پکیج tar.gz dexterity-0.0.9:
pip install dexterity-0.0.9.tar.gz