ROS Academy

The modular design of ROS (Robot Operating System) makes it arguably the most used software stack for robot developers. Software developed with ROS tools is becoming more and more common place for applications such as robot manipulators, service robots, drones, autonomous cars, etc. In this 2-day training participants with none to basic knowledge of ROS will learn the skills and competences to configure and use ROS-based software solutions.

Training Topics

This course was designed by the ROS experts of FH Aachen and is part of the ROSIN project. In this 2-day course tutors from TU Delft will teach you the basics of ROS-Industrial. You will work out hands-on sessions in simulation and on the real robots and learn about:

  • ROS File system
  • ROS Tools (RVIZ, RQT)
  • ROS Communication (Topics, Services and Actions)
  • Transformations (TF)
  • Robot Modelling (URDF)

Depending on the interest of the participants we will also cover some of the following topics:

  • Manipulation (MoveIt!)
  • Localization (AMCL & Gmapping)
  • Navigation (move_base)
  • Simulation (Gazebo)

After this training the participant should be able to design basic ROS software and know where to find useful information in the ROS ecosystem.


The training is open to anyone interested in programming robots with ROS. Participants need to have a basic knowledge of programming (preferably python), and some knowledge of Linux is useful. This course can be taught in Dutch or English (depending on the need). For more information contact Martin Klomp ( You can register for this course on the website of RoboHouse.

Costs: E500,- for two days (ex VAT, including lunch)

Upcoming training dates:

TBA (depends on COVID situation), Delft

It is also possible to schedule other on-request dates from 5 participants and more.

------------------------------------------------------------------------------------------------------------------------------Supported by ROSIN – ROS-Industrial Quality-Assured Robot Software Components.
More information:

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 732287.

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