Using Drake from Python

A limited subset of the Drake C++ functionality is available from Python. The Drake Python bindings are generated using pybind11, which means that every function or class which is exposed to C++ has been explicitly enumerated in one of the source files inside the bindings/pydrake folder. These bindings are installed as a single package called pydrake.

Python 2.7 is currently the only supported version for these bindings.

Building the Python Bindings

To use the Python bindings from Drake externally, we recommend using CMake. As an example:

git clone
mkdir drake-build
cd drake-build
cmake ../drake
make -j

Please note the additional CMake options which affect the Python bindings:

  • -DWITH_GUROBI={ON, [OFF]} - Build with Gurobi enabled.
  • -DWITH_MOSEK={ON, [OFF]} - Build with MOSEK enabled.
  • -DWITH_SNOPT={ON, [OFF]} - Build with SNOPT enabled.

{...} means a list of options, and the option surrounded by [...] is the default option. An example of building pydrake with both Gurobi and MOSEK, without building tests:


Using the Python Bindings

To use the Drake Python bindings, follow the build steps above or ensure that you have installed Drake appropriately. You will also need to have your PYTHONPATH configured correctly.

As an example, continuing from the code snippets from above:

cd drake-build
export PYTHONPATH=${PWD}/install/lib/python2.7/site-packages:${PYTHONPATH}

To check this:

python -c 'import pydrake; print(pydrake.__file__)'


If you are using macOS, you must ensure that you are using the python2 executable to run these scripts.

If you would like to use jupyter, then be sure to install it via pip2 install jupyter (not brew install jupyter) to ensure that it uses the correct PYTHONPATH.


If you are using Gurobi, you must either have it installed in the suggested location under /opt/... mentioned in Gurobi 8.0.0, or you must ensure that you define the ${GUROBI_PATH} environment variable, or specify ${GUROBI_INCLUDE_DIR} via CMake.

What’s Available from Python

The most up-to-date demonstrations of what can be done using pydrake are the pydrake unit tests themselves. You can see all of them inside the drake/bindings/python/pydrake/**/test folders in the Drake source code.

Here’s an example snippet of code from pydrake:

from pydrake.common import FindResourceOrThrow
from pydrake.multibody.rigid_body_plant import RigidBodyPlant
from pydrake.multibody.rigid_body_tree import RigidBodyTree
from import Simulator

tree = RigidBodyTree(
simulator = Simulator(RigidBodyPlant(tree))

If you are prototyping code in a REPL environment (such as IPython / Jupyter) and to reduce the number of import statements, consider using pydrake.all to import a subset of symbols from a flattened namespace or import all modules automatically. If you are writing non-prototype code, avoid using pydrake.all; for more details, see help(pydrake.all).

In all cases, try to avoid using from pydrake.all import *, as it may introduce symbol collisions that are difficiult to debug.

An example of importing symbols directly from pydrake.all:

from pydrake.all import (
    FindResourceOrThrow, RigidBodyPlant, RigidBodyTree, Simulator)

tree = RigidBodyTree(
simulator = Simulator(RigidBodyPlant(tree))

An alternative is to use pydrake.all to import all modules, but then explicity refer to each symbol:

import pydrake.all

tree = pydrake.multibody.rigid_body_tree.RigidBodyTree(
simulator =


There is not yet a comprehensive API documentation for the Python bindings (tracked by #7914).

In general, the Python API should be close to the C++ API. There are some exceptions:

C++ Template Instantiations in Python

When you define a general class template, e.g. template <typename T> class Value, something like Value<std::string> is called the instantiation.

For certain C++ templated types, they are exposed in Pythons also as templates; the parameter types (in this case, T) are the Python-equivalent types to the C++ type. Some examples:

C++ Python
std::string str
double float, np.double, np.float64, ctypes.c_double
drake::AutoDiffXd pydrake.autodiffutils.AutoDiffXd
drake::symbolic::Expression pydrake.symbolic.Expression

Thus, the instantiation Value<std::string> will be bound in Python as Value[str].

Scalar Types

Most classes in the Systems framework and in the multibody dynamics computational framework are templated on a scalar type, T. For convenience (and backwards compatibility) in Python, a slightly different binding convention is used.

For example, Adder<T> is a Systems primitive which has a user-defined number of inputs and outputs a single port which is the sum of all of the inputs.

In C++, you would access the instantiations using Adder<double>, Adder<AutoDiffXd>, and Adder<Expression> for common scalar types.

In Python, Adder actually refers to the “default” instantiation, the Adder<double> C++ class. To access other instantiations, you should add an _ to the end of the C++ class name to get the Python template and then provide the parameters in square braces, [...]. In this example, you should use Adder_[T].

To illustrate, you can print out the string representations of Adder, Adder_, and some of its instantiations in Python:

>>> from import Adder, Adder_
>>> print(Adder)
<class '[float]'>
>>> print(Adder_)
>>> from pydrake.autodiffutils import AutoDiffXd
>>> from pydrake.symbolic import Expression
>>> print(Adder_[float])
<class '[float]'>
>>> print(Adder_[AutoDiffXd])
<class '[AutoDiffXd]'>
>>> print(Adder_[Expression])
<class '[Expression]'>

Additionally, you may convert an instance (if the conversion is available) using System_[T].ToAutoDiffXd and System_[T].ToSymbolic:

>>> adder = Adder(num_inputs=1, size=1)
>>> print(adder)
<[float] object at 0x...>
>>> print(adder.ToAutoDiffXd())
<[AutoDiffXd] object at 0x...>
>>> print(adder.ToSymbolic())
<[Expression] object at 0x...>

For Developers

If you are developing Python bindings, please see the Doxygen page for Python Bindings. This provides information on programming conventions as well as tips for debugging.