Difference between revisions of "MuJoCo"
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Download models and example files from the official MuJoCo website or repository. Detailed documentation and setup instructions accompany each model. Users are encouraged to experiment, modify XML files, and share their findings with the community. | Download models and example files from the official MuJoCo website or repository. Detailed documentation and setup instructions accompany each model. Users are encouraged to experiment, modify XML files, and share their findings with the community. | ||
| + | |||
| + | By leveraging the MuJoCo Menagerie, researchers and engineers can accelerate their work, explore new possibilities, and contribute to the MuJoCo ecosystem. | ||
| + | |||
| + | |||
| + | === Getting Started === | ||
| + | |||
| + | To install the Menagerie, simply clone the repository in the directory of your choice: | ||
| + | <syntaxhighlight lang="bash"> | ||
| + | git clone https://github.com/google-deepmind/mujoco_menagerie.git | ||
| + | </syntaxhighlight> | ||
| + | |||
| + | The easiest way to interactively explore a model is to load it in the simulate binary which ships with every MuJoCo distribution. Just drag and drop the <code>scene.xml</code> file into the simulate window. Alternatively, you can use the command line to launch simulate and directly pass in the path to the XML. | ||
| + | |||
| + | Outside of interactive simulation, you can load a model exactly as you would with any other XML file in MuJoCo. Here’s how you do it with the C/C++ API: | ||
| + | |||
| + | <syntaxhighlight lang="c"> | ||
| + | #include <mujoco.h> | ||
| + | |||
| + | mjModel* model = mj_loadXML("unitree_a1/a1.xml", nullptr, nullptr, 0); | ||
| + | mjData* data = mj_makeData(model); | ||
| + | mj_step(model, data); | ||
| + | </syntaxhighlight> | ||
| + | |||
| + | And here’s how you do it with Python: | ||
| + | |||
| + | <syntaxhighlight lang="python"> | ||
| + | import mujoco | ||
| + | |||
| + | model = mujoco.MjModel.from_xml_path("unitree_a1/a1.xml") | ||
| + | data = mujoco.MjData(model) | ||
| + | mujoco.mj_step(model, data) | ||
| + | </syntaxhighlight> | ||
By leveraging the MuJoCo Menagerie, researchers and engineers can accelerate their work, explore new possibilities, and contribute to the MuJoCo ecosystem. | By leveraging the MuJoCo Menagerie, researchers and engineers can accelerate their work, explore new possibilities, and contribute to the MuJoCo ecosystem. | ||
Revision as of 16:25, 19 May 2024
Contents
MuJoCo
MuJoCo, short for Multi-Joint dynamics with Contact, is a physics engine designed for research and development in robotics, machine learning, and biomechanics. This open-source software provides accurate and efficient simulation of complex physical systems, making it highly regarded among researchers and engineers.
History
MuJoCo was developed at the University of Washington and released in 2012. Initially created to aid in the study of motor control in humans and robots, it quickly gained popularity for its ability to simulate complex physical interactions with high precision and efficiency. In 2021, DeepMind, a subsidiary of Alphabet Inc., acquired MuJoCo and made it freely available to the public.
Tips and Suggestions
When creating MuJoCo XML files, it can be helpful to start with a full robot model and then manually copy and paste sections to create single or dual-arm setups. This approach saves time and ensures consistency across different models.
Additionally, MuJoCo supports defining keyframes for specific poses directly in the XML, which can streamline the setup process for various robot configurations.
If you encounter issues with simulation stability, e.g. warnings about NaN values, consider fine-tuning the hyperparameters, including control gains and regularization terms, to achieve more stable and reliable simulations.
MuJoCo Menagerie
The MuJoCo Menagerie is a collection of pre-built models and simulation environments showcasing the capabilities of the MuJoCo physics engine. This resource is valuable for researchers, engineers, and enthusiasts exploring robotics, biomechanics, and machine learning applications.
Getting Started
Download models and example files from the official MuJoCo website or repository. Detailed documentation and setup instructions accompany each model. Users are encouraged to experiment, modify XML files, and share their findings with the community.
By leveraging the MuJoCo Menagerie, researchers and engineers can accelerate their work, explore new possibilities, and contribute to the MuJoCo ecosystem.
Getting Started
To install the Menagerie, simply clone the repository in the directory of your choice:
git clone https://github.com/google-deepmind/mujoco_menagerie.git
The easiest way to interactively explore a model is to load it in the simulate binary which ships with every MuJoCo distribution. Just drag and drop the scene.xml file into the simulate window. Alternatively, you can use the command line to launch simulate and directly pass in the path to the XML.
Outside of interactive simulation, you can load a model exactly as you would with any other XML file in MuJoCo. Here’s how you do it with the C/C++ API:
#include <mujoco.h>
mjModel* model = mj_loadXML("unitree_a1/a1.xml", nullptr, nullptr, 0);
mjData* data = mj_makeData(model);
mj_step(model, data);
And here’s how you do it with Python:
import mujoco
model = mujoco.MjModel.from_xml_path("unitree_a1/a1.xml")
data = mujoco.MjData(model)
mujoco.mj_step(model, data)
By leveraging the MuJoCo Menagerie, researchers and engineers can accelerate their work, explore new possibilities, and contribute to the MuJoCo ecosystem.
