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The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 hasdefined ambitious new benchmarks to advance the state-of-the-art in autonomousoperation of ground-based and flying robots. In this article, we describe ourwinning entry to MBZIRC Challenge 2: the mobile manipulation robot Mario. It iscapable of autonomously solving a valve manipulation task using a wrench tooldetected, grasped, and finally employed to turn a valve stem. Mario'somnidirectional base allows both fast locomotion and precise close approach tothe manipulation panel. We describe an efficient detector for medium-sizedobjects in 3D laser scans and apply it to detect the manipulation panel. Anobject detection architecture based on deep neural networks is used to find andselect the correct tool from grayscale images. Parametrized motion primitivesare adapted online to percepts of the tool and valve stem in order to turn thestem. We report in detail on our winning performance at the challenge anddiscuss lessons learned.

Team NimbRo at MBZIRC 2017: Autonomous Valve Stem Turning using a Wrench