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Abstract-Remote control of robots is often necessary to complete complex unstructured tasks in environments that are inaccessible (e.g. dangerous) for humans. Tele-operation of humanoid robots is often performed trough motion tracking to reduce the complexity deriving from manually controlling a high number of DOF. However, most commercial motion tracking apparatus are expensive and often uncomfortable. Moreover, a limitation of this approach is the need to maintain visual contact with the operated robot, or to employ a second human operator to independently maneuver a camera. As a result, even performing simple tasks heavily depends on the skill and synchronization of the two operators. To alleviate this problem we propose to use augmented-reality to provide the operator with first-person vision and a natural interface to directly control the camera, and at the same time the robot. By integrating recent off-the-shelf technologies, we provide an affordable and intuitive environment composed of Microsoft Kinect, Oculus Rift and haptic SensorGlove to tele-operate in first-person humanoid robots. We demonstrate on the humanoid robot iCub that this set-up allows to quickly and naturally accomplish complex tasks.

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Model synchronization, i.e., the task of restoring consistency between two interrelated models after a model change, is a challenging task. Triple Graph Grammars (TGGs) specify model consistency by means of rules. They can be used to automatically derive specifications of edit operations for single models and repair rules that propagate model changes to related models. model (re-)synchronization activities more effectively, a construction mechanism for shortcut rules has been recently developed. They describe consistency-preserving complex edit operations across model boundaries. We show that edit and repair rules can be derived from shortcut rules. As proof of concept, we implemented the construction and application of shortcut edit and repair rules in eMoflon. Our evaluation shows that shortcut rule based repair processes have considerably decreased data loss and improved runtime compared to former model synchronization processes in eMoflon.

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When using graph transformations to formalize model transformations, it is often desirable to design transformations that preserve consistency with respect to a given set of (model) integrity constraints. The standard approach is to equip transformations with suitable application conditions such that the introduction of constraint violations is prevented. This may lead to rules that are applicable seldom or even inapplicable at all, though. To supplement this approach, we present a new and systematic procedure to develop correct-by-construction transformations with respect to a special kind of constraints. Instead of controlling the applicability of a rule we complement its action in such a way that a given constraint holds after application: For every way in which the rule could introduce a violation of the constraint, we derive a supplementary action for the rule that remedies that violation. We formalize this construction in the setting of adhesive categories for monotonic rules and positive atomic constraints and present sufficient conditions for its correctness.

Stochastic models can be found in various domains. For example, biochemical processes such as molecular interactions or the dynamics of wireless network topologies, where changes occur with certain probabilities. Having the ability to simulate scenarios in these domains can be crucial when real-life observations of certain processes are infeasible, e.g., protein-protein interactions in biochemistry, or expensive, e.g., building large wireless networks for research purposes. Stochastic graph transformation systems provide the means to describe the structure and simulate the behavior of such probability-driven environments in an adequate way, by modelling the state transitions using graph transformation rules, whose application depends on the current state and their application probabilities. To the best of our knowledge, there is currently no general-purpose simulation tool available anymore that performs rule-based simulations using stochastic graph transformation. Therefore, we developed SimSG a modular stochastic simulation tool that addresses the needs of a wide range of application domains-in contrast to most specialized simulation tools that are limited to one domain only. To facilitate the versatility of the tool, SimSG can be configured to employ different general-purpose tools for incremental graph pattern matching (currently, Democles and Viatra). We evaluate SimSG based on two use cases: First, using an example of the biochemistry domain, we conduct a comparative evaluation against the domain-specific tool KaSim. Second, we underpin the general-purpose applicability of SimSG by analyzing the simulation of a wireless sensor network scenario.