S
Sven Hellbach
Researcher at Technische Universität Ilmenau
Publications - 33
Citations - 352
Sven Hellbach is an academic researcher from Technische Universität Ilmenau. The author has contributed to research in topics: Non-negative matrix factorization & Mobile robot. The author has an hindex of 9, co-authored 32 publications receiving 302 citations. Previous affiliations of Sven Hellbach include Citec & Honda.
Papers
More filters
Proceedings ArticleDOI
Task-level imitation learning using variance-based movement optimization
TL;DR: An imitation learning framework is presented, which allows the robot to learn the important elements of an observed movement task by application of probabilistic encoding with Gaussian Mixture Models and shows that the proposed system is suitable for transferring information from a human demonstrator to the robot.
Proceedings ArticleDOI
Large scale place recognition in 2D LIDAR scans using Geometrical Landmark Relations
TL;DR: Geometrical Landmark Relations (GLARE) is presented, which transform 2D laser scans into pose invariant histogram representations, which significantly outperforms state-of-the-art approaches in place recognition for large scale outdoor environments, while achieving similar results for indoor settings.
Journal ArticleDOI
An insect-inspired bionic sensor for tactile localization and material classification with state-dependent modulation.
TL;DR: A bionic, active tactile sensing system inspired by insect antennae that can be applied to detect tactile contact events of a wheeled robot, and how detrimental effects of robot velocity on antennal dynamics can be suppressed by state-dependent modulation of the input signals is presented.
Journal ArticleDOI
Sparse coding of human motion trajectories with non-negative matrix factorization
Christian Vollmer,Christian Vollmer,Sven Hellbach,Sven Hellbach,Julian Eggert,Horst-Michael Gross +5 more
TL;DR: It is shown that basis vectors can be extracted, which can be interpreted as short motion segments, and that the sparse representation can be used for classification of trajectories of a single joint, like the one of a hand, obtained by motion capturing.
Book ChapterDOI
Echo State Networks for Online Prediction of Movement Data --- Comparing Investigations
TL;DR: The idea is to predict movement data of persons moving in the local surroundings by understanding it as time series by using a black box model, which means that no further information is used than the past of the trajectory itself.