Model-Based Design and Verification for Robotics

A C++ / Python toolbox supported by the Toyota Research Institute.


Core Library

Modeling Dynamical


Solving Mathematical


Multibody Kinematics
and Dynamics



Drake (“dragon” in Middle English) is a C++ toolbox started by the Robot Locomotion Group at the MIT Computer Science and Artificial Intelligence Lab (CSAIL). The development team has now grown significantly, with core development led by the Toyota Research Institute. It is a collection of tools for analyzing the dynamics of our robots and building control systems for them, with a heavy emphasis on optimization-based design/analysis.

While there are an increasing number of simulation tools available for robotics, most of them function like a black box: commands go in, sensors come out. Drake aims to simulate even very complex dynamics of robots (e.g. including friction, contact, aerodynamics, …), but always with an emphasis on exposing the structure in the governing equations (sparsity, analytical gradients, polynomial structure, uncertainty quantification, …) and making this information available for advanced planning, control, and analysis algorithms. Drake provides an interface to Python to enable rapid-prototyping of new algorithms, and also aims to provide solid open-source implementations for many state-of-the-art algorithms. Finally, we hope Drake provides many compelling examples that can help people get started and provide much needed benchmarks. We are excited to accept user contributions to improve the coverage.

You can read more about the vision for Drake in this blog post.

We hope you find this tool useful. Please see Getting Help if you wish to share your comments, questions, success stories, or frustrations. And please contribute your best bug fixes, features, and examples!


We have Python tutorials that can be previewed and executed as Jupyter notebooks online with no need for local installation. You can use Binder to preview and execute the notebooks (but startup time may be long), or you can use nbviewer to only preview the notebook (where startup time is fast):

If you are browsing on nbviewer, you may click on the Binder button at the top-right of the page.

You may find more information about how to run these locally with Jupyter, the branch the tutorials use, how they are published to Binder, etc., in drake/tutorials/

Citing Drake

 author = "Russ Tedrake and the Drake Development Team",
 title = "Drake: Model-based design and verification for robotics",
 year = 2019,
 url = ""


We have a number of use cases demonstrated under drake/examples in the source tree, and more available through our Drake Gallery (contributions welcome!).

We also have a number of examples of using Drake as a external library in your own projects, including examples with various build systems and examples of how you might set up continuous integration.

From Other Languages

Python Bindings