Using Drake from Python

A substantial 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.


Drake is incompatible with the Python environment supplied by Anaconda. Please uninstall Anaconda or remove the Anaconda bin directory from the PATH before building or using the Drake Python bindings.


Before attempting installation, please review the supported configurations to know what versions of Python are supported for your platform.

Binary Installation for Python

First, download and extract an available binary package.

As an example, here is how to download and extract one of the latest releases to /opt (where <platform> could be bionic or mac):

curl -o drake.tar.gz<platform>.tar.gz
rm -rf /opt/drake
tar -xvzf drake.tar.gz -C /opt

Ensure that you have the system dependencies:


Next, ensure that your PYTHONPATH is properly configured. For example, for the Python 3 bindings on Bionic:

export PYTHONPATH=/opt/drake/lib/python3.6/site-packages:${PYTHONPATH}

See below for usage instructions. If using macOS, pay special attention to this note.

Inside virtualenv

At present, Drake is not installable via pip. However, you can still incorporate its install tree into a virtualenv FHS-like environment.

An example, where you should replace <venv_path> and <platform>:

# Setup drake, and run prerequisites.
curl -o drake.tar.gz<platform>.tar.gz
mkdir -p <venv_path>
tar -xvzf drake.tar.gz -C <venv_path> --strip-components=1
# - You may need `sudo` here.

# Setup a virtualenv over the drake install.
python3 -m virtualenv -p python3 <venv_path> --system-site-packages


You can extract Drake into an existing virtualenv tree if you have already run install_prereqs; however, you should ensure that you have run install_prereqs. Before you do this, you should capture / freeze your current requirements to reproduce your environment if there are conflicts.

To check if this worked, follow the instructions as shown below, but either:

  • Use <venv_path>/bin/python instead of python3, or
  • Source <venv_path>/bin/activate in your current shell session.

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:


You will also need to have your PYTHONPATH configured correctly.

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

cd drake-build
export PYTHONPATH=${PWD}/install/lib/python3.6/site-packages:${PYTHONPATH}

Using the Python Bindings

Check Installation

After following the above install steps, check to ensure you can import pydrake. As an example for Python 3:

python3 -c 'import pydrake; print(pydrake.__file__)'


If you are using Gurobi, you must either have it installed in the suggested location under /opt/... mentioned in Gurobi 8.0.1, 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.parsing import Parser
from pydrake.multibody.plant import AddMultibodyPlantSceneGraph
from import Simulator
from import DiagramBuilder

builder = DiagramBuilder()
plant, _ = AddMultibodyPlantSceneGraph(builder, 0.0)
diagram = builder.Build()
simulator = Simulator(diagram)

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 difficult to debug.

The above example, but using pydrake.all:

from pydrake.all import (
    AddMultibodyPlantSceneGraph, DiagramBuilder, FindResourceOrThrow,
    Parser, Simulator)

builder = DiagramBuilder()
plant, _ = AddMultibodyPlantSceneGraph(builder, 0.0)
diagram = builder.Build()
simulator = Simulator(diagram)

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

import pydrake.all

builder =
plant, _ = pydrake.multibody.plant.AddMultibodyPlantSceneGraph(builder, 0.0)
diagram = builder.Build()
simulator =

Differences with C++ API

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...>

Debugging with the Python Bindings

You may encounter issues with the Python Bindings that may arise from the underlying C++ code, and it may not always be obvious what the root cause is.

The first step to debugging is to consider running your code using the trace module. It is best practice to always have a main() function, and have a if __name__ == "__main__" clause. If you do this, then it is easy to trace. As an example:

def main():

if __name__ == "__main__":
    # main()  # Normal invocation; commented out, because we will trace it.

    # The following (a) imports minimum dependencies, (b) ensures that
    # output is immediately flushed (e.g. for segfaults), and (c) traces
    # execution of your function, but filtering out any Python code outside
    # of the system prefix.
    import sys, trace
    sys.stdout = sys.stderr
    tracer = trace.Trace(trace=1, count=0, ignoredirs=["/usr", sys.prefix])


If you are developing in Drake and are using the drake_py_unittest macro, you can specify the argument --trace=user to get the same behavior.

This generally should help you trace where the code is dying. However, if you still need to dig in, you can build the bindings in debug mode, without symbol stripping, so you can debug with gdb or lldb:

cmake -DCMAKE_BUILD_TYPE=Debug ../drake


If you have SNOPT enabled (either -DWITH_SNOPT=ON or -DWITH_ROBOTLOCOMOTION_SNOPT=ON), symbols will still be stripped.

For Developers

If you are developing Python bindings, please see the Doxygen page Python Bindings which provides information on programming conventions, documentation, tips for debugging, and other advice.