  The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has
defined ambitious new benchmarks to advance the state-of-the-art in autonomous
operation of ground-based and flying robots. In this article, we describe our
winning entry to MBZIRC Challenge 2: the mobile manipulation robot Mario. It is
capable of autonomously solving a valve manipulation task using a wrench tool
detected, grasped, and finally employed to turn a valve stem. Mario's
omnidirectional base allows both fast locomotion and precise close approach to
the manipulation panel. We describe an efficient detector for medium-sized
objects in 3D laser scans and apply it to detect the manipulation panel. An
object detection architecture based on deep neural networks is used to find and
select the correct tool from grayscale images. Parametrized motion primitives
are adapted online to percepts of the tool and valve stem in order to turn the
stem. We report in detail on our winning performance at the challenge and
discuss lessons learned.
