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Version 2.1.2: Python bindings, OBJ assets support, bugfixes.

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===============
Python Bindings
===============
Introduction
------------
Starting with version 2.1.2, MuJoCo comes with native Python bindings that are developed in C++ using
`pybind11 <https://pybind11.readthedocs.io/>`__. Unlike previous Python bindings, these are officially supported by the
MuJoCo development team and will be kept up-to-date with the latest developments in MuJoCo itself.
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.
DeepMind’s `dm_control <https://github.com/deepmind/dm_control>`__ reinforcement learning library (which prior to
version 1.0.0 implemented its own MuJoCo bindings based on ``ctypes``) has been updated to depend on the ``mujoco``
package and continues to be supported by DeepMind. Changes in dm_control should be largely transparent to users of
previous versions, however code that depended directly on its low-level API may need to be updated. Consult the
`migration guide <https://github.com/deepmind/dm_control/blob/main/migration_guide_1.0.md>`__ for detail.
For mujoco-py users, we include :ref:`notes <PyMjpy_migration>` below to aid migration.
Installation
------------
The package can be installed from `PyPI <https://pypi.org/project/mujoco/>`__ via
.. 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.
Building from source
--------------------
Source code for the Python bindings are available in the ``python`` top-level directory in MuJoCo's
`GitHub repository <https://github.com/deepmind/mujoco>`__. Developers wishing to build the bindings from source should
work with a full clone of the Git repository, run the ``make_sdist.sh`` script to generate a
`source distribution (sdist) <https://packaging.python.org/en/latest/glossary/#term-Source-Distribution-or-sdist>`__
tarball, then run ``pip wheel name_of_sdist.tar.gz`` to build the libraries and generate a
`wheel <https://packaging.python.org/en/latest/glossary/#term-Built-Distribution>`__. The ``make_sdist.sh`` script
generates additional C++ header files that are needed to build the bindings, and also pulls in other required files from
elsewhere in the repository outside the ``python`` directory into the sdist.
CMake and a C++17 compiler are needed to build the bindings from source.
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
=======
MuJoCo data structures are exposed as Python classes. 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 ``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.
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.
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``.
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. 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')``.
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. (This is currently **not** the
case for the :ref:`mj_name2id` function, which performs a linear scan.)
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``
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.
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 utilises 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.
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.
.. _PyMjpy_migration:
Migration Notes for mujoco-py
-----------------------------
In mujoco-py, the main entry point is the `MjSim <https://github.com/openai/mujoco-py/blob/master/mujoco_py/mjsim.pyx>`_
class. Users constuct 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>` whose current implementation is inefficient. ``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``.
Code Sample: open-loop rollout
------------------------------
We include a code sample showing how to add additional C/C++ functionality, exposed as a Python module via pybind11. The
sample, implemented in ``rollout.cc`` and wrapped in ``rollout.py``, implements a common use case where tight loops
implemented outside of Python are beneficial: rolling out a trajectory (i.e., calling ``mj_step()`` in a loop), given an
intial state and sequence of controls, and returning subsequent states and sensor values. The canonical usage form is
.. code-block:: python
state, sensordata = rollout.rollout(model, data, initial_state, ctrl)
``initial_state`` is a ``nstate x nqva`` array, with ``nstate`` initial states of length ``nqva``, where ``nqva =
model.nq + model.nv + model.na`` is the size of the full MuJoCo mechanical state: positions (``data.qpos``), velocities
(``data.qvel``) and actuator activations (``data.act``). ``ctrl`` is a ``nstate x nstep x nu`` array of control
sequences.
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. In order to facilitate this,
all inputs including ``time`` and ``qacc_warmstart`` are set to default values, as are auxillary controls
(``qfrc_applied``, ``xfrc_applied`` and ``mocap_{pos,quat}``). These can also be optionally set by the user.
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`` for an example of threaded operation.