Introduction As part of a cooperation project with the Deutsches Elektronen-Synchrotron (DESY), a safe, learning-enabled control framework is to be developed for an autonomous mobile robot that is to be used for radiation measurement and maintenance tasks in particle accelerator tunnels. It should be able to recognize obstacles independently, avoid collisions with them and carry out tasks independently. Conventional robotics approaches work with predefined trajectories, which restrict them to an isolated work area and a controlled environment free of obstacles [1].