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MuJoCo is an advanced simulator for multi-body dynamics with contact. It was developed by Roboti LLC and was available as a commercial product from 2015 to 2021.

We are excited to announce that as of October 2021, DeepMind has acquired MuJoCo and is making it freely available to everyone under the Apache 2.0 license. MuJoCo 2.1 has been released as unlocked binaries, available at mujoco.org along with updated documentation. MuJoCo will be open-sourced in the coming months and its future home will be github.com/deepmind/mujoco. We encourage all users to follow the project there. In the meantime, mujoco.org will remain the project's home page.

Roboti LLC will continue to support existing paid licenses until they expire. Legacy MuJoCo releases (versions 2.0 and earlier) will remain available for download, with a free activation key file that is valid until October 2031.

The Python bindings are distributed as the mujoco package on PyPI. These arelow-level bindings that are meant to give as close to a direct access to the MuJoCo library as possible. However, inorder to provide an API and semantics that developers would expect in a typical Python library, the bindingsdeliberately diverge from the raw MuJoCo API in a number of places, which are documented throughout this page.

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An interactive GUI viewer is provided as part of the Python package in the mujoco.viewer module. It is based on thesame codebase as the simulate application that ships with the MuJoCo binary releases. Three distinctuse cases are supported:

On MacOS, launch_passive requires that the user script is executed via a special mjpython launcher.The mjpython command is installed as part of the mujoco package, and can be used as a drop-in replacementfor the usual python command and supports an identical set of command line flags and arguments. For example,a script can be executed via mjpython my_script.py, and an IPython shell can be launched viamjpython -m IPython.

All structs other than mjModel have constructors in Python. For structs that have an mj_defaultFoo-styleinitialization function, the Python constructor calls the default initializer automatically, so for examplemujoco.MjOption() creates a new mjOption instance that is pre-initialized with mj_defaultOption.Otherwise, the Python constructor zero-initializes the underlying C struct.

Structs with a mj_makeFoo-style initialization function have corresponding constructor overloads in Python,for example mujoco.MjvScene(model, maxgeom=10) in Python creates a new mjvScene instance that isinitialized with mjv_makeScene(model, [the new mjvScene instance], 10) in C. When this form of initialization isused, the corresponding deallocation function mj_freeFoo/mj_deleteFoo is automatically called when the Pythonobject is deleted. The user does not need to manually free resources.

The mujoco.MjModel class does not a have Python constructor. Instead, we provide three static factory functionsthat create a new mjModel instance: mujoco.MjModel.from_xml_string, mujoco.MjModel.from_xml_path, andmujoco.MjModel.from_binary_path. The first function accepts a model XML as a string, while the latter twofunctions accept the path to either an XML or MJB model file. All three functions optionally accept a Pythondictionary which is converted into a MuJoCo Virtual file system for use during model compilation.

MuJoCo functions are exposed as Python functions of the same name. Unlike with structs, we do not attempt to makethe function names PEP 8-compliant, as MuJoCo uses both underscores andCamelCases. In most cases, function arguments appear exactly as they do in C, and keyword arguments are supportedwith the same names as declared in mujoco.h. Python bindings to C functions that accept array inputarguments expect NumPy arrays or iterable objects that are convertible to NumPy arrays (e.g. lists). Outputarguments (i.e. array arguments that MuJoCo expect to write values back to the caller) must always be writeableNumPy arrays.

MuJoCo enums are available as mujoco.mjtEnumType.ENUM_VALUE, for example mujoco.mjtObj.mjOBJ_SITE. MuJoCoconstants are available with the same name directly under the mujoco module, for example mujoco.mjVISSTRING.

For each name category foo, mujoco.MjModel and mujoco.MjData objects provide a method foo that takesa single string argument, and returns an accessor object for all arrays corresponding to the entity foo of thegiven name. The accessor object contains attributes whose names correspond to the fields of either mujoco.MjModel ormujoco.MjData but with the part before the underscore removed. In addition, accessor objects also provide id andname properties, which can be used as replacements for mj_name2id and mj_id2name respectively. For example:

m.geom('gizmo').rgba is a NumPy array view of length 4 that specifies the RGBA color for the geom.Specifically, it corresponds to the portion of m.geom_rgba[4*i:4*i+4] wherei = mujoco.mj_name2id(m, mujoco.mjtObj.mjOBJ_GEOM, 'gizmo').

MuJoCo itself expects users to set up a working OpenGL context before calling any of its mjr_ rendering routine.The Python bindings provide a basic class mujoco.GLContext that helps users set up such a context for offscreenrendering. To create a context, call ctx = mujoco.GLContext(max_width, max_height). Once the context is created,it must be made current before MuJoCo rendering functions can be called, which you can do so via ctx.make_current().Note that a context can only be made current on one thread at any given time, and all subsequent rendering calls must bemade on the same thread.

The Python bindings utilizes longjmp to allow it to convert irrecoverable MuJoCo errors into Python exceptions of typemujoco.FatalError that can be caught and processed in the usual Pythonic way. Furthermore, it installs its errorcallback in a thread-local manner using a currently private API, thus allowing for concurrent calls into MuJoCo frommultiple threads.

MuJoCo allows users to install custom callback functions to modify certain parts of its computation pipeline.For example, mjcb_sensor can be used to implement custom sensors, and mjcb_control can be used toimplement custom actuators. Callbacks are exposed through the function pointers prefixed mjcb_ inmujoco.h.

Alternatively, if a callback is implemented in a native dynamic library, users can usectypes to obtain a Python handle to the C function pointer and passit to mujoco.set_mjcb_foo. The bindings will then retrieve the underlying function pointer and assign it directly tothe raw callback pointer, and the GIL will not be acquired each time the callback is entered.

In mujoco-py, the main entry point is the MjSimclass. Users construct a stateful MjSim instance from an MJCF model (similar to dm_control.Physics), and thisinstance holds references to an mjModel instance and its associated mjData. In contrast, the MuJoCo Pythonbindings (mujoco) take a more low-level approach, as explained above: following the design principle of the Clibrary, the mujoco module itself is stateless, and merely wraps the underlying native structs and functions.

This factory function constructs a stateful MjSim instance. When using mujoco, the user should call thefactory function mujoco.MjModel.from_xml_* as described above. The user is then responsiblefor holding the resulting MjModel struct instance and explicitly generating the corresponding MjData bycalling mujoco.MjData(model).

Here as above, mujoco users needs to call the underlying library functions, passing instances of MjModel andMjData: mujoco.mj_resetData(model, data), mujoco.mj_forward(model, data), and mujoco.mj_step(model, data).

The make_sdist.sh script generates additional C++ header files that areneeded to build the bindings, and also pulls in required files from elsewherein the repository outside the python directory into the sdist. Uponcompletion, the script will create a dist directory with amujoco-x.y.z.tar.gz file (where x.y.z is the version number).

Try -controls/ros_c... for non-gazebo usage of the gazebo control plugins you are using, you should be able to start up standalone nodes that publish out the results of their control computations on topics, then you could have a python node subscribe to those and talk to mujoco.

These environments also require that the MuJoCo engine be installed. As of October 2021 DeepMind has acquired MuJoCo and is open-sourcing it in 2022, making it free for everyone. Instructions on installing the MuJoCo engine can be found on their website and GitHub repository. Using MuJoCo with Gymnasium also requires that the framework mujoco be installed (this dependency is installed with the above command).

These environments also require that the MuJoCo engine be installed. As of October 2021 DeepMind has acquired MuJoCo and is open sourcing it in 2022, making it free for everyone. Instructions on installing the MuJoCo engine can be found at their website and GitHub repository. Using MuJoCo with OpenAI Gym also requires that the framework mujoco-py be installed, which can be found at the GitHub repository (this dependency in installed with the above command).

Note that you can specify a command-line argument when running the program from a desktop shortcut: right-click the shortcut, select Properties, then select the Shortcut tab and enter the command-line argument after the program name in the Target field.

Running MuJoCo HAPTIX without "-nomocap" shows a welcome screen and displays progress of the OptiTrack initialization - which either succeeds, or fails with an error message. Possible reasons for failure are: The OptiTrack is disconnected or powered down, Motive or another instance of MuJoCo HAPTIX is already running, The file "project.ttp" was not found in the program directory, or is corrupted, The expected names and number of rigid bodies and/or markers were not found in "project.ttp", NPTrackingToolsx64.dll or libiomp5md.dll was not found.The latter two DLLs are the OptiTrack library and Intel's OpenMP library which the OptiTrack software uses. MuJoCo HAPTIX looks for them under their default installation directory (Program Files\OptiTrack\Motive) as well as in the system path. If Motive was installed at its default location these DLLs will be found automatically. If for whatever reason they are not found, locate them manually and copy them to the MuJoCo HAPTIX program directory. libiomp5md.dll has 32-bit and 64-bit versions. We need the 64-bit version.

If any of the above error conditions are encountered, the program shows an error message and continues with the motion capture-related Toolbar items disabled (same as if it was started with "-nomocap"). In this mode it can still be used over the API as well as with the interactive mouse controls and SpaceNavigator. This is useful if you want to work on a second computer that does not have an OptiTrack connected to it. Do not install Motive on this second computer; it will not run without OptiTrack hardware.

MuJoCo HAPTIX has a socket API which requires opening a socket for listening and accepting connections from user programs. The first time you run the software, Windows will automatically show the following dialog:

Click "Allow access". This will create an Inbound rule in the Windows Firewall the program to listen for incoming connections. If you click Cancel, it will create a rule blocking connections - in which case you can still connect to it from the local machine but not from a remote machine.

The software has a built-in mechanism for update notifications. On startup it connects to www.mujoco.org, parses the webpage listing the available versions, and prints a message in the lower-right corner indicating if a new version is available or if the software is up to date. This mechanism will not work if your computer is not connected to the Internet or you have firewall policies in place that prevent the software from accessing the Internet. Updating is done manually. The automated mechanism described here only shows notifications, and is designed to remain as unobtrusive as possible; the notification disappears as soon as you load a model. 2351a5e196