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Auflistung nach Autor:in "Behnke, Sven"

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  • Zeitschriftenartikel
    Deep Learning
    (KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Schulz, Hannes; Behnke, Sven
    Hierarchical neural networks for object recognition have a long history. In recent years, novel methods for incrementally learning a hierarchy of features from unlabeled inputs were proposed as good starting point for supervised training. These deep learning methods—together with the advances of parallel computers—made it possible to successfully attack problems that were not practical before, in terms of depth and input size. In this article, we introduce the reader to the basic concepts of deep learning, discuss selected methods in detail, and present application examples from computer vision and speech recognition.
  • Zeitschriftenartikel
    Online Learning of Bipedal Walking Stabilization
    (KI - Künstliche Intelligenz: Vol. 29, No. 4, 2015) Missura, Marcell; Behnke, Sven
    Bipedal walking is a complex whole-body motion with inherently unstable dynamics that makes the design of a robust controller particularly challenging. While a walk controller could potentially be learned with the hardware in the loop, the destructive nature of exploratory motions and the impracticality of a high number of required repetitions render most of the existing machine learning methods unsuitable for an online learning setting with real hardware. In a project in the DFG Priority Programme Autonomous Learning, we are investigating ways of bootstrapping the learning process with basic walking skills and enabling a humanoid robot to autonomously learn how to control its balance during walking.
  • Zeitschriftenartikel
    Rough Terrain 3D Mapping and Navigation Using a Continuously Rotating 2D Laser Scanner
    (KI - Künstliche Intelligenz: Vol. 28, No. 2, 2014) Schadler, Mark; Stückler, Jörg; Behnke, Sven
    Mapping, real-time localization, and path planning are prerequisites for autonomous robot navigation. These functions also facilitate situation awareness of remote operators. In this paper, we propose methods for efficient 3D mapping and real-time 6D pose tracking of autonomous robots using a continuously rotating 2D laser scanner. We have developed our approach in the context of the DLR SpaceBot Cup robotics challenge. Multi-resolution surfel representations allow for compact maps and efficient registration of local maps. Real-time pose tracking is performed by a particle filter observing individual laser scan lines. Terrain drivability is assessed within a global environment map and used for planning feasible paths. Our approach is evaluated using challenging real environments.
  • Zeitschriftenartikel
    The igus Humanoid Open Platform
    (KI - Künstliche Intelligenz: Vol. 30, No. 0, 2016) Allgeuer, Philipp; Farazi, Hafez; Ficht, Grzegorz; Schreiber, Michael; Behnke, Sven
    The use of standard robotic platforms can accelerate research and lower the entry barrier for new research groups. There exist many affordable humanoid standard platforms in the lower size ranges of up to 60 cm, but larger humanoid robots quickly become less affordable and more difficult to operate, maintain and modify. The igus® Humanoid Open Platform is a new and affordable, fully open-source humanoid platform. At 92 cm in height, the robot is capable of interacting in an environment meant for humans, and is equipped with enough sensors, actuators and computing power to support researchers in many fields. The structure of the robot is entirely 3D printed, leading to a lightweight and visually appealing design. The main features of the platform are described in this article.