Learning Virtual Borders through Semantic Scene Understanding and Augmented Reality Virtual borders are an opportunity to allow users the interactive restriction of their mobile robotsxe2x80x99 workspaces, e.g. to avoid navigation errors or to exclude certain areas from working. Currently, works in this field have focused on humanrobot interaction (HRI) methods to restrict the workspace. However, recent trends towards smart environments and the tremendous progress in semantic scene understanding give new opportunities to enhance the HRIbased methods. Therefore, we propose a novel learning and support system (LSS) to support users during teaching of virtual borders. Our LSS learns from user interactions employing methods from visual scene understanding and supports users through recommendations for interactions. The bidirectional interaction between the user and system is realized using augmented reality. A validation of the approach shows that the LSS robustly recognizes a limited set of typical areas for virtual borders based on previous user interactions (F 1  Score 91.5%) while preserving the high accuracy of standard HRIbased methods with a median of Mdn 84.6%. Moreover, this approach allows the reduction of the interaction time to a constant mean value of M 2 seconds making it independent of the border length. This avoids a linear interaction time of standard HRIbased methods.

Simulationbased physics reasoning for consistent scene estimation in an HRI context Reasoning about spatial and geometric relations between objects in a tabletop humanrobot interaction is a challenge due to the perception not being always consistent: objects placed on a table seem to be slightly in the air; they overlap; they disappear due to occlusions. Yet, interpreting and anchoring perceptual data in a physically consistent estimation of the scene is a crucial ability for humans, and thus robots in HRI context. In this paper we present a simulationbased physics reasoner integrated in a lightweight situationassessment framework called Underworlds, that allows the robot to stabilize objects and build at runtime a consistent estimation of the scene, even for entirely hidden objects, while inferring the actions performed by its human partner.