Loading files
This file is pinned to the selected release. Download the original to open it in your design tool.
7fb444268This file is pinned to the selected release. Download the original to open it in your design tool.
======
Python
======
MuJoCo comes with native Python bindings that are developed in C++ using
`pybind11 <https://pybind11.readthedocs.io/>`__. The Python API is consistent with the underlying C API. This leads to
some non-Pythonic code structure (e.g. order of function arguments), but it has the benefit that the
:doc:`API documentation<APIreference/index>` is applicable to both languages.
The Python bindings are distributed as the ``mujoco`` package on `PyPI <https://pypi.org/project/mujoco>`__. These are
low-level bindings that are meant to give as close to a direct access to the MuJoCo library as possible. However, in
order to provide an API and semantics that developers would expect in a typical Python library, the bindings
deliberately diverge from the raw MuJoCo API in a number of places, which are documented throughout this page.
Google DeepMind’s `dm_control <https://github.com/google-deepmind/dm_control>`__ reinforcement learning library depends
on the ``mujoco`` package and continues to be supported by Google DeepMind. For code that depends on dm_control versions
prior to 1.0.0, consult the
`migration guide <https://github.com/google-deepmind/dm_control/blob/main/migration_guide_1.0.md>`__.
For mujoco-py users, we include :ref:`migration notes <PyMjpy_migration>` below.
.. _PyNotebook:
Tutorial notebook
=================
A MuJoCo tutorial using the Python bindings is available here: |mjcolab|
.. |mjcolab| image:: https://colab.research.google.com/assets/colab-badge.svg
:target: https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/tutorial.ipynb
.. _PyInstallation:
Installation
============
The recommended way to install this package is via `PyPI <https://pypi.org/project/mujoco/>`__:
.. code-block:: shell
pip install mujoco
A copy of the MuJoCo library is provided as part of the package and does **not** need to be downloaded or installed
separately.
.. _PyViewer:
Interactive viewer
==================
An interactive GUI viewer is provided as part of the Python package in the ``mujoco.viewer`` module. It is based on the
same codebase as the :ref:`simulate<saSimulate>` application that ships with the MuJoCo binary releases. Three distinct
use cases are supported:
.. _PyViewerApp:
Standalone app
--------------
- ``python -m mujoco.viewer`` launches an empty visualization session, where a model can be loaded by drag-and-drop.
- ``python -m mujoco.viewer --mjcf=/path/to/some/mjcf.xml`` launches a visualization session for the specified
model file.
.. _PyViewerManaged:
Managed viewer
--------------
Called from a Python program/script, through the function ``viewer.launch``. This function *blocks user code* to
support precise timing of the physics loop. This mode should be used if user code is implemented as
:ref:`engine plugins<exPlugin>` or :ref:`physics callbacks<glPhysics>`, and is called by MuJoCo during :ref:`mj_step`.
- ``viewer.launch()`` launches an empty visualization session, where a model can be loaded by drag-and-drop.
- ``viewer.launch(model)`` launches a visualization session for the given ``mjModel`` where the visualizer
internally creates its own instance of ``mjData``
- ``viewer.launch(model, data)`` is the same as above, except that the visualizer operates directly on the given
``mjData`` instance -- upon exit the ``data`` object will have been modified.
.. _PyViewerPassive:
Passive viewer
--------------
By calling ``viewer.launch_passive(model, data)``. This function *does not block*, allowing user code to continue
execution. In this mode, the user's script is responsible for timing and advancing the physics state, and mouse-drag
perturbations will not work unless the user explicitly synchronizes incoming events.
.. warning::
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 replacement
for 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 via
``mjpython -m IPython``.
The ``launch_passive`` function returns a handle which can be used to interact with the viewer. It has the following
attributes:
- ``cam``, ``opt``, and ``pert`` properties: correspond to :ref:`mjvCamera`, :ref:`mjvOption`, and :ref:`mjvPerturb`
structs, respectively.
- ``lock()``: provides a mutex lock for the viewer as a context manager. Since the viewer operates its own
thread, user code must ensure that it is holding the viewer lock before modifying any physics or visualization
state. These include the ``mjModel`` and ``mjData`` instance passed to ``launch_passive``, and also the ``cam``,
``opt``, and ``pert`` properties of the viewer handle.
- ``sync()``: synchronizes state between ``mjModel``, ``mjData``, and GUI user inputs since the previous call to
``sync``. In order to allow user scripts to make arbitrary modifications to ``mjModel`` and ``mjData`` without
needing to hold the viewer lock, the passive viewer does not access or modify these structs outside of ``sync``
calls.
User scripts must call ``sync`` in order for the viewer to reflect physics state changes. The ``sync`` function
also transfers user inputs from the GUI back into ``mjOption`` (inside ``mjModel``) and ``mjData``, including
enable/disable flags, control inputs, and mouse perturbations.
- ``update_hfield(hfieldid)``: updates the height field data at the specified ``hfieldid`` for subsequent renderings.
- ``update_mesh(meshid)``: updates the mesh data at the specified ``meshid`` for subsequent renderings.
- ``update_texture(texid)``: updates the texture data at the specified ``texid`` for subsequent renderings.
- ``close()``: programmatically closes the viewer window. This method can be safely called without locking.
- ``is_running()``: returns ``True`` if the viewer window is running and ``False`` if it is closed.
This method can be safely called without locking.
- ``user_scn``: an :ref:`mjvScene` object that allows users to add change rendering flags and add custom
visualization geoms to the rendered scene. This is separate from the ``mjvScene`` that the viewer uses internally to
render the final scene, and is entirely under the user's control. User scripts can call e.g. :ref:`mjv_initGeom` or
:ref:`mjv_makeConnector` to add visualization geoms to ``user_scn``, and upon the next call to ``sync()``, the viewer
will incorporate these geoms to future rendered images. Similarly, user scripts can make changes to ``user_scn.flags``
which would be picked up at the next call to ``sync()``. The ``sync()`` call also copies changes to rendering flags
made via the GUI back into ``user_scn`` to preserve consistency. For example:
.. code-block:: python
with mujoco.viewer.launch_passive(m, d, key_callback=key_callback) as viewer:
# Enable wireframe rendering of the entire scene.
viewer.user_scn.flags[mujoco.mjtRndFlag.mjRND_WIREFRAME] = 1
viewer.sync()
while viewer.is_running():
...
# Step the physics.
mujoco.mj_step(m, d)
# Add a 3x3x3 grid of variously colored spheres to the middle of the scene.
viewer.user_scn.ngeom = 0
i = 0
for x, y, z in itertools.product(*((range(-1, 2),) * 3)):
mujoco.mjv_initGeom(
viewer.user_scn.geoms[i],
type=mujoco.mjtGeom.mjGEOM_SPHERE,
size=[0.02, 0, 0],
pos=0.1*np.array([x, y, z]),
mat=np.eye(3).flatten(),
rgba=0.5*np.array([x + 1, y + 1, z + 1, 2])
)
i += 1
viewer.user_scn.ngeom = i
viewer.sync()
...
The viewer handle can also be used as a context manager which calls ``close()`` automatically upon exit. A minimal
example of a user script that uses ``launch_passive`` might look like the following. (Note that example is a simple
illustrative example that does **not** necessarily keep the physics ticking at the correct wallclock rate.)
.. code-block:: python
import time
import mujoco
import mujoco.viewer
m = mujoco.MjModel.from_xml_path('/path/to/mjcf.xml')
d = mujoco.MjData(m)
with mujoco.viewer.launch_passive(m, d) as viewer:
# Close the viewer automatically after 30 wall-seconds.
start = time.time()
while viewer.is_running() and time.time() - start < 30:
step_start = time.time()
# mj_step can be replaced with code that also evaluates
# a policy and applies a control signal before stepping the physics.
mujoco.mj_step(m, d)
# Example modification of a viewer option: toggle contact points every two seconds.
with viewer.lock():
viewer.opt.flags[mujoco.mjtVisFlag.mjVIS_CONTACTPOINT] = int(d.time % 2)
# Pick up changes to the physics state, apply perturbations, update options from GUI.
viewer.sync()
# Rudimentary time keeping, will drift relative to wall clock.
time_until_next_step = m.opt.timestep - (time.time() - step_start)
if time_until_next_step > 0:
time.sleep(time_until_next_step)
Optionally, ``viewer.launch_passive`` accepts the following keyword arguments.
- ``key_callback``: A callable which gets called each time a keyboard event occurs in the viewer window. This allows
user scripts to react to various key presses, e.g., pause or resume the run loop when the spacebar is pressed.
.. code-block:: python
paused = False
def key_callback(keycode):
if chr(keycode) == ' ':
nonlocal paused
paused = not paused
...
with mujoco.viewer.launch_passive(m, d, key_callback=key_callback) as viewer:
while viewer.is_running():
...
if not paused:
mujoco.mj_step(m, d)
viewer.sync()
...
- ``show_left_ui`` and ``show_right_ui``: Boolean arguments indicating whether UI panels should be visible
or hidden when the viewer is launched. Note that regardless of the values specified, the user can still toggle the
visibility of these panels after launch by pressing Tab or Shift+Tab.
.. _PyUsage:
Basic usage
===========
Once installed, the package can be imported via ``import mujoco``. Structs, functions, constants, and enums are
available directly from the top-level ``mujoco`` module.
.. _PyStructs:
Structs
-------
The bindings include Python classes that expose MuJoCo data structures. For maximum performance, these classes provide
access to the raw memory used by MuJoCo without copying or buffering. This means that some MuJoCo functions (e.g.,
:ref:`mj_step`) change the content of fields *in place*. The user is therefore advised to create copies where required.
For example, when logging the position of a body, one could write
``positions.append(data.body('my_body').xpos.copy())``. Without the ``.copy()``, the list would contain identical
elements, all pointing to the most recent value.
In order to conform to `PEP 8 <https://peps.python.org/pep-0008/>`__
naming guidelines, struct names begin with a capital letter, for example ``mjData`` becomes ``mujoco.MjData`` in Python.
All structs other than ``mjModel`` have constructors in Python. For structs that have an ``mj_defaultFoo``-style
initialization function, the Python constructor calls the default initializer automatically, so for example
``mujoco.MjOption()`` creates a new ``mjOption`` instance that is pre-initialized with :ref:`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 is
initialized with ``mjv_makeScene(model, [the new mjvScene instance], 10)`` in C. When this form of initialization is
used, the corresponding deallocation function ``mj_freeFoo/mj_deleteFoo`` is automatically called when the Python
object 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 functions
that create a new :ref:`mjModel` instance: ``mujoco.MjModel.from_xml_string``, ``mujoco.MjModel.from_xml_path``, and
``mujoco.MjModel.from_binary_path``. The first function accepts a model XML as a string, while the latter two
functions accept the path to either an XML or MJB model file. All three functions optionally accept a Python
dictionary which is converted into a MuJoCo :ref:`Virtualfilesystem` for use during model compilation.
.. _PyFunctions:
Functions
---------
MuJoCo functions are exposed as Python functions of the same name. Unlike with structs, we do not attempt to make
the function names `PEP 8 <https://peps.python.org/pep-0008/>`__-compliant, as MuJoCo uses both underscores and
CamelCases. In most cases, function arguments appear exactly as they do in C, and keyword arguments are supported
with the same names as declared in :ref:`mujoco.h<inHeader>`. Python bindings to C functions that accept array input
arguments expect NumPy arrays or iterable objects that are convertible to NumPy arrays (e.g. lists). Output
arguments (i.e. array arguments that MuJoCo expect to write values back to the caller) must always be writeable
NumPy arrays.
In the C API, functions that take dynamically-sized arrays as inputs expect a pointer argument to the array along with
an integer argument that specifies the array's size. In Python, the size arguments are omitted since we can
automatically (and indeed, more safely) deduce it from the NumPy array. When calling these functions, pass all
arguments other than array sizes in the same order as they appear in :ref:`mujoco.h<inHeader>`, or use keyword
arguments. For example, :ref:`mj_jac` should be called as ``mujoco.mj_jac(m, d, jacp, jacr, point, body)`` in Python.
The bindings **releases the Python Global Interpreter Lock (GIL)** before calling the underlying MuJoCo function.
This allows for some thread-based parallelism, however users should bear in mind that the GIL is only released for the
duration of the MuJoCo C function itself, and not during the execution of any other Python code.
.. note::
One place where the bindings do offer added functionality is the top-level :ref:`mj_step` function. Since it is
often called in a loop, we have added an additional ``nstep`` argument, indicating how many times the underlying
:ref:`mj_step` should be called. If not specified, ``nstep`` takes the default value of 1. The following two code
snippets perform the same computation, but the first one does so without acquiring the GIL in between subsequent
physics steps:
.. code-block:: python
mj_step(model, data, nstep=20)
.. code-block:: python
for _ in range(20):
mj_step(model, data)
.. _PyEnums:
Enums and constants
-------------------
MuJoCo enums are available as ``mujoco.mjtEnumType.ENUM_VALUE``, for example ``mujoco.mjtObj.mjOBJ_SITE``. MuJoCo
constants are available with the same name directly under the ``mujoco`` module, for example ``mujoco.mjVISSTRING``.
.. _PyExample:
Minimal example
---------------
.. code-block:: python
import mujoco
XML=r"""
<mujoco>
<asset>
<mesh file="gizmo.stl"/>
</asset>
<worldbody>
<body>
<freejoint/>
<geom type="mesh" name="gizmo" mesh="gizmo"/>
</body>
</worldbody>
</mujoco>
"""
ASSETS=dict()
with open('/path/to/gizmo.stl', 'rb') as f:
ASSETS['gizmo.stl'] = f.read()
model = mujoco.MjModel.from_xml_string(XML, ASSETS)
data = mujoco.MjData(model)
while data.time < 1:
mujoco.mj_step(model, data)
print(data.geom_xpos)
.. _PyNamed:
Named access
------------
Most well-designed MuJoCo models assign names to objects (joints, geoms, bodies, etc.) of interest. When the model is
compiled down to an ``mjModel`` instance, these names become associated with numeric IDs that are used to index into the
various array members. For convenience and code readability, the Python bindings provide "named access" API on
``MjModel`` and ``MjData``. Each ``name_fooadr`` field in the ``mjModel`` struct defines a name category ``foo``.
For each name category ``foo``, ``mujoco.MjModel`` and ``mujoco.MjData`` objects provide a method ``foo`` that takes
a single string argument, and returns an accessor object for all arrays corresponding to the entity ``foo`` of the
given name. The accessor object contains attributes whose names correspond to the fields of either ``mujoco.MjModel`` or
``mujoco.MjData`` but with the part before the underscore removed. In addition, accessor objects also provide ``id`` and
``name`` properties, which can be used as replacements for ``mj_name2id`` and ``mj_id2name`` respectively. For example:
- ``m.geom('gizmo')`` returns an accessor for arrays in the ``MjModel`` object ``m`` associated with the geom named
"gizmo".
- ``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]`` where
``i = mujoco.mj_name2id(m, mujoco.mjtObj.mjOBJ_GEOM, 'gizmo')``.
- ``m.geom('gizmo').id`` is the same number as returned by ``mujoco.mj_name2id(m, mujoco.mjtObj.mjOBJ_GEOM, 'gizmo')``.
- ``m.geom(i).name`` is ``'gizmo'``, where ``i = mujoco.mj_name2id(m, mujoco.mjtObj.mjOBJ_GEOM, 'gizmo')``.
Additionally, the Python API define a number of aliases for some name categories corresponding to the XML element name
in the MJCF schema that defines an entity of that category. For example, ``m.joint('foo')`` is the same as
``m.jnt('foo')``. A complete list of these aliases are provided below.
The accessor for joints is somewhat different that of the other categories. Some ``mjModel`` and ``mjData`` fields
(those of size size ``nq`` or ``nv``) are associated with degrees of freedom (DoFs) rather than joints. This is because
different types of joints have different numbers of DoFs. We nevertheless associate these fields to their corresponding
joints, for example through ``d.joint('foo').qpos`` and ``d.joint('foo').qvel``, however the size of these arrays would
differ between accessors depending on the joint's type.
Named access is guaranteed to be O(1) in the number of entities in the model. In other words, the time it takes to
access an entity by name does not grow with the number of names or entities in the model.
For completeness, we provide here a complete list of all name categories in MuJoCo, along with their corresponding
aliases defined in the Python API.
- ``body``
- ``jnt`` or ``joint``
- ``geom``
- ``site``
- ``cam`` or ``camera``
- ``light``
- ``mesh``
- ``skin``
- ``hfield``
- ``tex`` or ``texture``
- ``mat`` or ``material``
- ``pair``
- ``exclude``
- ``eq`` or ``equality``
- ``tendon`` or ``ten``
- ``actuator``
- ``sensor``
- ``numeric``
- ``text``
- ``tuple``
- ``key`` or ``keyframe``
.. _PyRender:
Rendering
---------
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 offscreen
rendering. 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 be
made on the same thread.
The context is freed automatically when the ``ctx`` object is deleted, but in some multi-threaded scenario it may be
necessary to explicitly free the underlying OpenGL context. To do so, call ``ctx.free()``, after which point it is the
user's responsibility to ensure that no further rendering calls are made on the context.
Once the context is created, users can follow MuJoCo's standard rendering, for example as documented in the
:ref:`Visualization` section.
.. _PyError:
Error handling
--------------
MuJoCo reports irrecoverable errors via the :ref:`mju_error` mechanism, which immediately terminates the entire process.
Users are permitted to install a custom error handler via the :ref:`mju_user_error` callback, but it too is expected
to terminate the process, otherwise the behavior of MuJoCo after the callback returns is undefined. In actuality, it is
sufficient to ensure that error callbacks do not return *to MuJoCo*, but it is permitted to use
`longjmp <https://en.cppreference.com/w/c/program/longjmp>`__ to skip MuJoCo's call stack back to the external callsite.
The Python bindings utilizes longjmp to allow it to convert irrecoverable MuJoCo errors into Python exceptions of type
``mujoco.FatalError`` that can be caught and processed in the usual Pythonic way. Furthermore, it installs its error
callback in a thread-local manner using a currently private API, thus allowing for concurrent calls into MuJoCo from
multiple threads.
.. _PyCallbacks:
Callbacks
---------
MuJoCo allows users to install custom callback functions to modify certain parts of its computation pipeline.
For example, :ref:`mjcb_sensor` can be used to implement custom sensors, and :ref:`mjcb_control` can be used to
implement custom actuators. Callbacks are exposed through the function pointers prefixed ``mjcb_`` in
:ref:`mujoco.h<inHeader>`.
For each callback ``mjcb_foo``, users can set it to a Python callable via ``mujoco.set_mjcb_foo(some_callable)``. To
reset it, call ``mujoco.set_mjcb_foo(None)``. To retrieve the currently installed callback, call
``mujoco.get_mjcb_foo()``. (The getter **should not** be used if the callback is not installed via the Python bindings.)
The bindings automatically acquire the GIL each time the callback is entered, and release it before reentering MuJoCo.
This is likely to incur a severe performance impact as callbacks are triggered several times throughout MuJoCo's
computation pipeline and is unlikely to be suitable for "production" use case. However, it is expected that this feature
will be useful for prototyping complex models.
Alternatively, if a callback is implemented in a native dynamic library, users can use
`ctypes <https://docs.python.org/3/library/ctypes.html>`__ to obtain a Python handle to the C function pointer and pass
it to ``mujoco.set_mjcb_foo``. The bindings will then retrieve the underlying function pointer and assign it directly to
the raw callback pointer, and the GIL will **not** be acquired each time the callback is entered.
.. _PyModelEdit:
Model editing
=============
The :doc:`Model Editing<programming/modeledit>` framework which allows for procedural model manipulation is exposed
via Python. In many ways this API is conceptually similar to ``dm_control``'s
`PyMJCF module <https://github.com/google-deepmind/dm_control/tree/main/dm_control/mjcf#readme>`__, where ``MjSpec``
plays the role of ``mjcf_model``. The largest difference between these two APIs is speed. Native model manipulation via
``MjSpec`` is around ~100x faster than PyMJCF.
Below is a simple example of how to use the model editing API. For more examples, please refer to
`specs_test.py <https://github.com/google-deepmind/mujoco/blob/main/python/mujoco/specs_test.py>`__.
.. code-block:: python
import mujoco
spec = mujoco.MjSpec()
body = spec.worldbody.add_body()
body.pos = [1, 2, 3]
body.quat = [0, 1, 0, 0]
geom = body.add_geom()
geom.name = 'my_geom'
geom.type = mujoco.mjtGeom.mjGEOM_SPHERE
geom.size[0] = 1
geom.rgba = [1, 0, 0, 1]
...
model = spec.compile()
.. admonition:: Missing features
:class: attention
We are aware of multiple missing features in the Python API, including:
- Convenient constructors like:
|br| :python:`geom = body.add_geom(name='my_geom', size=[1, 1, 1], rgba=[1, 0, 0, 1])`
- Better tree traversal utilities like :python:`children = body.children()` etc.
- PyMJCF's notion of "binding", allowing access to :ref:`mjModel` and :ref:`mjData` values via the associated ``mjs``
elements.
There are certainly other missing features that we are not aware of. Please contact us on GitHub with feature
requests or bug reports and we will prioritize accordingly.
.. _PyBuild:
Building from source
====================
.. note::
Building from source is only necessary if you are modifying the
Python bindings (or are trying to run on exceptionally old Linux systems).
If that's not the case, then we recommend installing the prebuilt binaries
from PyPI.
1. Make sure you have CMake and a C++17 compiler installed.
2. Download the `latest binary release <https://github.com/google-deepmind/mujoco/releases>`__
from GitHub. On macOS, the download corresponds to a DMG file from which you
can drag ``MuJoCo.app`` into your ``/Applications`` folder.
3. Clone the entire ``mujoco`` repository from GitHub and ``cd`` into the python
directory:
.. code-block:: shell
git clone https://github.com/google-deepmind/mujoco.git
cd mujoco/python
4. Create a virtual environment:
.. code-block:: shell
python3 -m venv /tmp/mujoco
source /tmp/mujoco/bin/activate
5. Generate a `source distribution <https://packaging.python.org/en/latest/glossary/#term-Source-Distribution-or-sdist>`__
tarball with the ``make_sdist.sh`` script.
.. code-block:: shell
cd python
bash make_sdist.sh
The ``make_sdist.sh`` script generates additional C++ header files that are
needed to build the bindings, and also pulls in required files from elsewhere
in the repository outside the ``python`` directory into the sdist. Upon
completion, the script will create a ``dist`` directory with a
``mujoco-x.y.z.tar.gz`` file (where ``x.y.z`` is the version number).
6. Use the generated source distribution to build and install the bindings.
You'll need to specify the path to the MuJoCo library you downloaded earlier
in the ``MUJOCO_PATH`` environment variable.
.. note::
For macOS, this can be the path to a directory that contains the
``mujoco.framework``. In particular, you can set
``MUJOCO_PATH=/Applications/MuJoCo.app`` if you installed MuJoCo as
suggested in step 1.
.. code-block:: shell
cd dist
MUJOCO_PATH=/PATH/TO/MUJOCO
MUJOCO_PLUGIN_PATH=/PATH/TO/MUJOCO_PLUGIN
pip install mujoco-x.y.z.tar.gz
The Python bindings should now be installed! To check that they've been
successfully installed, ``cd`` outside of the ``mujoco`` directory and run
``python -c "import mujoco"``.
.. tip::
As a reference, a working build configuration can be found in MuJoCo's
`continuous integration setup <https://github.com/google-deepmind/mujoco/blob/main/.github/workflows/build.yml>`_ on
GitHub.
.. _PyModule:
Modules
=======
The ``mujoco`` package contains two sub-modules: ``mujoco.rollout`` and ``mujoco.minimize``
.. _PyRollout:
rollout
-------
``mujoco.rollout`` shows how to add additional C/C++ functionality, exposed as a Python module via pybind11. It is
implemented in `rollout.cc <https://github.com/google-deepmind/mujoco/blob/main/python/mujoco/rollout.cc>`__
and wrapped in `rollout.py <https://github.com/google-deepmind/mujoco/blob/main/python/mujoco/rollout.py>`__. The module
performs a common functionality where tight loops implemented outside of Python are beneficial: rolling out a trajectory
(i.e., calling :ref:`mj_step` in a loop), given an intial state and sequence of controls, and returning subsequent
states and sensor values. The basic usage form is
.. code-block:: python
state, sensordata = rollout.rollout(model, data, initial_state, control)
``initial_state`` is an ``nroll x nstate`` array, with ``nroll`` initial states of size ``nstate``, where
``nstate = mj_stateSize(model, mjtState.mjSTATE_FULLPHYSICS)`` is the size of the
:ref:`full physics state<geFullPhysics>`. ``control`` is a ``nroll x nstep x ncontrol`` array of controls. Controls are
by default the ``mjModel.nu`` standard actuators, but any combination of :ref:`user input<geInput>` arrays can be
specified by passing an optional ``control_spec`` bitflag.
If a rollout diverges, the current state and sensor values are used to fill the remainder of the trajectory.
Therefore, non-increasing time values can be used to detect diverged rollouts.
The ``rollout`` function is designed to be completely stateless, so all inputs of the stepping pipeline are set and any
values already present in the given ``MjData`` instance will have no effect on the output.
Since the Global Interpreter Lock can be released, this function can be efficiently threaded using Python threads. See
the ``test_threading`` function in
`rollout_test.py <https://github.com/google-deepmind/mujoco/blob/main/python/mujoco/rollout_test.py>`__ for an example
of threaded operation (and more generally for usage examples).
.. _PyMinimize:
minimize
--------
This module contains optimization-related utilities.
The ``minimize.least_squares()`` function implements a nonlinear Least Squares optimizer solving sequential
Quadratic Programs with :ref:`mju_boxQP`. It is documented in the associated notebook: |lscolab|
.. |lscolab| image:: https://colab.research.google.com/assets/colab-badge.svg
:target: https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/least_squares.ipynb
.. _PyUtility:
Utilities
=========
The `python/mujoco <https://github.com/google-deepmind/mujoco/tree/main/python/mujoco>`__ directory also contains
utility scripts.
.. _PyMsh2obj:
msh2obj.py
----------
The `msh2obj.py <https://github.com/google-deepmind/mujoco/blob/main/python/mujoco/msh2obj.py>`__ script converts the
:ref:`legacy .msh format<legacy-msh-docs>` for surface meshes (different from the possibly-volumetric
:ref:`gmsh format<gmsh-file-docs>` also using .msh), to OBJ files. The legacy format is depricated and will be removed
in a future release. Please convert all legacy files to OBJ.
.. _PyMjpy_migration:
mujoco-py migration
===================
In mujoco-py, the main entry point is the `MjSim <https://github.com/openai/mujoco-py/blob/master/mujoco_py/mjsim.pyx>`_
class. Users construct a stateful ``MjSim`` instance from an MJCF model (similar to ``dm_control.Physics``), and this
instance holds references to an ``mjModel`` instance and its associated ``mjData``. In contrast, the MuJoCo Python
bindings (``mujoco``) take a more low-level approach, as explained above: following the design principle of the C
library, the ``mujoco`` module itself is stateless, and merely wraps the underlying native structs and functions.
While a complete survey of mujoco-py is beyond the scope of this document, we offer below implementation notes for a
non-exhaustive list of specific mujoco-py features:
``mujoco_py.load_model_from_xml(bstring)``
This factory function constructs a stateful ``MjSim`` instance. When using ``mujoco``, the user should call the
factory function ``mujoco.MjModel.from_xml_*`` as described :ref:`above <PyStructs>`. The user is then responsible
for holding the resulting ``MjModel`` struct instance and explicitly generating the corresponding ``MjData`` by
calling ``mujoco.MjData(model)``.
``sim.reset()``, ``sim.forward()``, ``sim.step()``
Here as above, ``mujoco`` users needs to call the underlying library functions, passing instances of ``MjModel`` and
``MjData``: :ref:`mujoco.mj_resetData(model, data) <mj_resetData>`, :ref:`mujoco.mj_forward(model, data)
<mj_forward>`, and :ref:`mujoco.mj_step(model, data) <mj_step>`.
``sim.get_state()``, ``sim.set_state(state)``, ``sim.get_flattened_state()``, ``sim.set_state_from_flattened(state)``
The MuJoCo library’s computation is deterministic given a specific input, as explained in the :ref:`Programming
section <Simulation>`. mujoco-py implements methods for getting and setting some of the relevant fields (and
similarly ``dm_control.Physics`` offers methods that correspond to the flattened case). ``mujoco`` do not offer such
abstraction, and the user is expected to get/set the values of the relevant fields explicitly.
``sim.model.get_joint_qvel_addr(joint_name)``
This is a convenience method in mujoco-py that returns a list of contiguous indices corresponding to this joint. The
list starts from ``model.jnt_qposadr[joint_index]``, and its length depends on the joint type. ``mujoco`` doesn't
offer this functionality, but this list can be easily constructed using ``model.jnt_qposadr[joint_index]`` and
``xrange``.
``sim.model.*_name2id(name)``
mujoco-py creates dicts in ``MjSim`` that allow for efficient lookup of indices for objects of different types:
``site_name2id``, ``body_name2id`` etc. These functions replace the function :ref:`mujoco.mj_name2id(model,
type_enum, name) <mj_name2id>`. ``mujoco`` offers a different approach for using entity names – :ref:`named access
<PyNamed>`, as well as access to the native :ref:`mj_name2id`.
``sim.save(fstream, format_name)``
This is the one context in which the MuJoCo library (and therefore also ``mujoco``) is stateful: it holds a copy in
memory of the last XML that was compiled, which is used in :ref:`mujoco.mj_saveLastXML(fname) <mj_saveLastXML>`. Note
that mujoco-py’s implementation has a convenient extra feature, whereby the pose (as determined by ``sim.data``’s
state) is transformed to a keyframe that’s added to the model before saving. This extra feature is not currently
available in ``mujoco``.