Artificial Intelligence for Kinematics, Dynamics, and Optimization
C++ Python CMake M4 Shell Makefile C
Latest commit 6a94d4b Jan 31, 2017 @jslee02 jslee02 committed on GitHub Merge pull request #133 from personalrobotics/fix_vdc_constructor
fix missing argument to VanDerCorput constructor

AIKIDO - AI for KIDO Build Status Coverage Status

⚠️ Warning: AIKIDO is under heavy development. These instructions are primarily for reference by the developers.

AIKIDO is a C++ library, complete with Python bindings, for solving robotic motion planning and decision making problems. This library is tightly integrated with DART for kinematic/dynamics calculations and OMPL for motion planning. AIKIDO optionally integrates with ROS, through the suite of aikido_ros packages, for execution on real robots.


AIKIDO depends on CMake, Boost, DART (version 6.1 or above), OMPL, and the Python development headers (python-dev on Debian systems). DART and AIKIDO both make heavy use of C++11 and require a modern compiler.

Installation (Standalone)

Once the dependencies are installed, you can build AIKIDO using CMake:

$ mkdir build
$ cd build
$ cmake ..
$ make
$ sudo make install

Installation (Catkin)

It is also possible to build AIKIDO as a third-party package inside a Catkin workspace. To do so, clone AIKIDO into your Catkin workspace and use the catkin build command like normal.

If you are using the older catkin_make command, then you must build your workspace with catkin_make_isolated. This may dramatically increase your build time, so we strongly recommend that you use catkin build, which is provided by the catkin_tools package, if possible.


Aikido is licensed under a BSD license. See LICENSE for more information.


Aikido is developed by the Personal Robotics Lab in the Robotics Institute at Carnegie Mellon University. The library was started by Michael Koval (@mkoval) and Pras Velagapudi (@psigen). It has received major contributions from Shushman Choudhury (@Shushman), Aaron Johnson (@aaronjoh), Jennifer King (@jeking), Gilwoo Lee (@lgw903), and Clint Liddick (@ClintLiddick). We also would like to thank Michael Grey (@mxgrey) and J.S. Lee (@jslee02) for making changes to DART to better support Aikido.