Surface treatment tasks such as grinding, sanding or polishing are a vital step of the value chain in many industries, but are notoriously challenging to automate. We present RoboGrind, an integrated system for the intuitive, interactive automation of surface treatment tasks with industrial robots. It combines a sophisticated 3D perception pipeline for surface scanning and automatic defect identification, an interactive voice-controlled wizard system for the AI-assisted bootstrapping and parameterization of robot programs, and an automatic planning and execution pipeline for force-controlled robotic surface treatment. RoboGrind is evaluated both under laboratory and real-world conditions in the context of refabricating fiberglass wind turbine blades.

We present a comprehensive comparison of hybrid Data-Driven Control (DDC) applied on a hydraulic excavator. DDC offers a state-of-the-art, high performance control based on data and expert knowledge. On the one hand, expert knowledge is complex to adapt to each unique excavator requiring substantial engineering efforts. On the other hand, purely data based control overcomes this drawback by adapting a Neural Network (NN) inverse control directly on measured input/output data. Yet, coverage of the entire phase space and extrapolation to unknown situations is challenging for solely data-driven approaches. On a real demonstrator, we analyze expert white box methods, solely data-driven black box approaches and a hybrid grey box approach which combines a data-driven and simplified expert model. We examine trajectory tracking performance, engineering effort and safe exploration as goal criteria. Besides various experiments for testing safety, we apply a Support-Vector-Machine (SVM) to analyze the extrapolation fitness of the data-driven components to unknown data.