The deployment of versatile robot systems in diverse environments requires intuitive approaches for humans to flexibly teach them new skills. In our present work, we investigate different user feedback types to teach a real robot a new movement skill. We compare feedback as star ratings on an absolute scale for single roll-outs versus preference-based feedback for pairwise comparisons with respective optimization algorithms (i.e., a variation of co-variance matrix adaptationevolution strategy (CMA-ES) and random optimization) to teach the robot the game of skill cup-and-ball. In an experimental investigation with users, we investigated the influence of the feedback type on the user experience of interacting with the different interfaces and the performance of the learning systems. While there is no significant difference for the subjective user experience between the conditions, there is a significant difference in learning performance. The preference-based system learned the task quicker, but this did not influence the users' evaluation of it. In a follow-up study, we confirmed that the difference in learning performance indeed can be attributed to the human users' performance.

The research of European history across various time layers gives insights about the development of the European cultural identity. Nuremberg as one of the great European metropolises during the Middle Ages experienced a number of transformations throughout the centuries. Within the TRANSRAZ research project, Nuremberg and the development of its architecture and culture is recreated from the 17th to the 21st century. It will be available for researchers and the public by means of an interactive 3D environment. Goal of this poster paper is to discuss the ongoing work of connecting heterogeneous historical data from sources previously hidden in archives to the 3D model using knowledge graphs for a scientifically accurate exploration of Nuremberg. The contribution of this paper is the Nuremberg Address Knowledge Graph (NA-KG) which contains information of people and organizations in Nuremberg from unstructured data of Nuremberg address books.

Historical archival records present many challenges for OCR systems to correctly encode their content, due to visual complexity, e.g. mixed printed text and handwritten annotations, paper degradation and faded ink. This paper addresses the problem of automatic identification and separation of handwritten and printed text in historical archival documents, including the creation of an artificial pixel-level annotated dataset and the presentation of a new FCN-based model trained on historical data. Initial test results indicate 18% IoU performance improvement on recognition of printed pixels and 10%IoU performance improvement on recognition of handwritten pixels in synthesised data when compared to the state-of-the-art trained on modern documents. Furthermore, an extrinsic OCR-based evaluation on the printed layer extracted from real historical documents shows 26% performance increase.