Software and tasks for dexterous multi-fingered hand manipulation, powered by MuJoCo
Project description
The MuJoCo Dexterity Suite
Software and tasks for dexterous multi-fingered hand manipulation, powered by MuJoCo.
dexterity
builds on 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:
Installation
PyPI (Recommended)
The recommended way to install this package is via PyPI:
pip install dexterity
Source
We use Python 3.8 and Miniconda for development. To create an environment and install dependencies, run the following steps:
conda env create -f environment.yml # Creates a dexterity env.
conda activate dexterity
pip install -e ".[dev]"
Overview
The MuJoCo dexterity
suite is composed of the following core components:
models
: MuJoCo models for dexterous hands and PyMJCF classes for dynamically customizing them.inverse_kinematics
: Inverse kinematics solver for multi-fingered hands.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.
dexterity
also comes pre-packaged with a suite of benchmark RL environments. Our hope is to grow it over time with crowd-sourced contributions from the research community.
Task Name | Description | Preview |
---|---|---|
reach |
Successively controlling the fingers of the hand to reach goal locations in 3D space. | |
reorient |
Reorienting the OpenAI cube to a goal orientation. |
Roadmap
- Add support for bi-manual tasks.
- Add more tasks to the suite.
Acknowledgements
A large part of the design and implementation of dexterity
is inspired by the MoMa library in dm_robotics.
Project details
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