Starting with version 2.1.2, MuJoCo comes with native Python bindings that are developed in C++ using pybind11. 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. 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 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 for detail.
For mujoco-py users, we include :ref:`notes <PyMjpy_migration>` below to aid migration.
The package can be installed from PyPI via
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.
Source code for the Python bindings are available in the python
top-level directory in MuJoCo's
GitHub repository. 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)
tarball, then run pip wheel name_of_sdist.tar.gz
to build the libraries and generate a
wheel. 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.
Once installed, the package can be imported via import mujoco
. Structs, functions, constants, and enums are
available directly from the top-level mujoco
module.
MuJoCo data structures are exposed as Python classes. In order to conform to PEP 8 naming guidelines, struct names begin with a capital letter, for example
mjData
becomesmujoco.MjData
in Python.All structs other than
mjModel
have constructors in Python. For structs that have anmj_defaultFoo
-style initialization function, the Python constructor calls the default initializer automatically, so for examplemujoco.MjOption()
creates a newmjOption
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 examplemujoco.MjvScene(model, maxgeom=10)
in Python creates a newmjvScene
instance that is initialized withmjv_makeScene(model, [the new mjvScene instance], 10)
in C. When this form of initialization is used, the corresponding deallocation functionmj_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 newmjModel
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 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.
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-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:mj_step(model, data, nstep=20)for _ in range(20): mj_step(model, data)
MuJoCo enums are available asmujoco.mjtEnumType.ENUM_VALUE
, for examplemujoco.mjtObj.mjOBJ_SITE
. MuJoCo constants are available with the same name directly under themujoco
module, for examplemujoco.mjVISSTRING
.
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)
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 theMjModel
objectm
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 ofm.geom_rgba[4*i:4*i+4]
wherei = mujoco.mj_name2id(m, mujoco.mjtObj.mjOBJ_GEOM, 'gizmo')
.m.geom('gizmo').id
is the same number as returned bymujoco.mj_name2id(m, mujoco.mjtObj.mjOBJ_GEOM, 'gizmo')
.m.geom(i).name
is'gizmo'
, wherei = 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
orjoint
geom
site
cam
orcamera
light
mesh
skin
hfield
tex
ortexture
mat
ormaterial
pair
exclude
eq
orequality
tendon
orten
actuator
sensor
numeric
text
tuple
key
orkeyframe
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.
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 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.
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 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.
In mujoco-py, the main entry point is the MjSim
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:
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)
.
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.
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
.
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`.
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
.
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
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.