Combined efforts in the fields of neuroscience, computer science and biology allowed to design biologically realistic models of the brain based on spiking neural networks. For a proper validation of these models, an embodiment in a dynamic and rich sensory environment, where the model is exposed to a realistic sensory-motor task, is needed. Due to the complexity of these brain models that, at the current stage, cannot deal with real-time constraints, it is not possible to embed them into a real world task. Rather, the embodiment has to be simulated as well. While adequate tools exist to simulate either complex neural networks or robots and their environments, there is so far no tool that allows to easily establish a communication between brain and body models. The Neurorobotics Platform is a new web-based environment that aims to filling this gap by offering scientists and technology developers a software infrastructure allowing them to connect brain models to detailed simulations of robot bodies and environments and to use the resulting neurorobotic systems for in-silico experimentation. In order to simplify the workflow and reduce the level of the required programming skills, the platform provides editors for the specification of experimental sequences and conditions, envi-ronments, robots, and brain-body connectors. In addition to that, a variety of existing robots and environments are provided. This work presents the architecture of the first release of the Neurorobotics Platform developed in subproject 10 “Neurorobotics” of the Human Brain Project (HBP). At the current state, the Neurorobotics Platform allows researchers to design and run basic experiments in neurorobotics using simulated robots and simulated environments linked to simplified versions of brain models. We illustrate the capabilities of the platform with three example experiments: a Braitenberg task implemented on a mobile robot, a sensory-motor learning task based on a robotic controller and a visual tracking embedding a retina model on the iCub humanoid robot. These use-cases allow to assess the applicability of the Neurorobotics Platform for robotic tasks as well as in neuroscientific experiments.

We report on recent progress in the iterative process of exploring, evaluating and refining Augmented Reality-based methods to support the order picking process. We present our findings from three user studies and from demonstrations at several exhibitions. The resulting setup is a combined visualization to precisely and efficiently guide the user, even if the augmentation is not always in the field of view of the HMD.

It is a common problem of AR applications that optical see-through head-mounted displays (OST-HMD) move on users' heads or are even temporarily taken off, thus requiring frequent (re)calibrations. If such calibrations involve user interactions, they are time consuming and distract users from their applications. Furthermore, they inject user-dependent errors into the system setup and reduce users' acceptance of OST-HMDs. To overcome these problems, we present a method that utilizes dynamic 3D eye position measurements from an eye tracker in combination with pre-computed, static display calibration parameters. Our experiments provide a comparison of our calibration with SPAAM (Single Point Active Alignment Method) for several head-display conditions: in the first condition, repeated calibrations are conducted while keeping the display position on the user's head fixed. In the second condition, users take the HMD off and put it back on in between calibrations. The result shows that our new calibration with eye tracking is more stable than repeated SPAAM calibrations. We close with a discussion on potential error sources which should be removed to achieve higher calibration quality.

Augmented reality deals with the problem of dynamically augmenting or enhancing (images or live video of) the real world with computer generated data (e.g., graphics of vir- tual objects). This poses two major problems: (a) deter- mining the precise alignment of real and virtual coordinate frames for overlay, and (b) capturing the 3D environment in- cluding camera and object motions. The latter is important for interactive augmented reality applications where users can interact with both real and virtual objects. Here we address the problem of accurately tracking the 3D motion of a monocular camera in a known 3D environ- ment and dynamically estimating the 3D camera location. We utilize fully automated landmark-based camera calibra- tion to initialize the motion estimation and employ extended Kalman filter techniques to track landmarks and to estimate the camera location. The implementation of our approach has been proven to be efficient and robust and our system successfully tracks in real-time at approximately 15 Hz.