Matthias Platho,
"Learning of sensory representations for task-dependent control of search processes",
Learning of sensory representations for task-dependent control of search processes, University Mannheim, 2010.
Abstract
Fulfilling demanding tasks in complex, dynamic environments poses a tough challenge for nowadays systems. A system relying on visual information has to handle a lot of data concerning the environment, the task and relevant objects. In order to reduce the amount of data a compression method for object datasets is proposed, that aims for maintaining task performance. An object dataset comprises numerical representations of object features in differ...
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Robert Kastner, Thomas Michalke, Jannik Fritsch, Christian Goerick,
"Towards a Task Dependent Representation Generation for Scene Analysis",
IEEE Intelligent Vehicles Symposium (IV), 2010.
Abstract
State-of-the-art advanced driver assistance systems (ADAS) typically focus on single tasks and therefore, have clearly defined functionalities. Although said ADAS functions (e.g. lane departure warning) show good performance, they lack the general ability to extract spatial relations of the environment. These spatial relations are required for scene analysis on a higher layer of abstraction, providing a new quality of scene understanding, e.g. fo...
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Mathias Franzius and Heiko Wersing,
"Learning Invariant Visual Shape Representations from Physics",
ICANN (3), pp. 298-302, 2010.
Abstract
3D shape determines an object’s physical properties to a large degree. In this article, we introduce an autonomous learning system for categorizing 3D shape of simulated objects from single views. The system extends an unsupervised bottom-up learning architecture based on the slowness principle with top-down information derived from the physical behavior of objects. The unsupervised bottom-up learning leads to pose invariant representations. Sh...
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Gökhan Ince, Kazuhiro Nakadai, Tobias Rodemann, Hiroshi Tsujino, Jun-ichi Imura,
"Robust Ego Noise Suppression of a Robot",
2010.
Abstract
This paper describes an architecture that can enhance a robot with the capability of performing automatic speech recognition even while the robot is moving. The system consists of three blocks: (1) a multi-channel noise reduction block comprising consequent stages of microphone-array-based sound localization, geometric source separa- tion and post filtering, (2) a single-channel template subtraction block and (3) a speech recognition block. In th...
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Raphael Golombek, Sebastian Wrede, Marc Hanheide, Martin Heckmann,
"A Method for learning a Fault Detection Model from Component Communication Data in Robotic Systems",
Proc. 7th IARP Workshop on Technical Challenges for Dependable Robots in Human Environments, 2010.
Abstract
A promising means to increase the dependability of a robotic system is to equip it with the ability to autonomously monitor it own system state and detect faults. In this contribution we propose a method for fault detection in robotic systems which exploits the concept of anomaly detection and learns a model based on dynamics in the system’s internal exchange of data. Learning a model reduces the need for expert system-knowledge and enables on-...
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Daniel Dornbusch, Robert Haschke, Stefan Menzel, Heiko Wersing,
"Finding Correlations in Multimodal Data Using Decomposition Approaches",
European Symposium on Artificial Neural Networks (ESANN), pp. 253 – 258, 2010.
Abstract
In this paper, we propose the application of standard decomposition approaches to find local correlations in multimodal data. In a test scenario, we apply these methods to correlate the local shape of turbine blades with their associated aerodynamic flow fields. We compare several decomposition algorithms, i.e., k-Means, Principal Component Analysis, Non-negative Matrix Factorization and Non-Negative Sparse Coding, with regards to their efficienc...
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Vitali Anselm,
"Receding Horizon Control for Anticipatory Movement Generation of a Robot Arm",
Technical University of Dortmund, Chair for Control and Systems Engineering, 2010.
Abstract
The focus of this diploma thesis is set on the trajectory adaptation for robots with regard to dynamical changing situations. The existing system, which generates the movements of the robot by a batch-approach with sequential flow, is improved in such way that it becomes a system with a reactive behaviour. This work deploys the concepts of a predictive control, namely of the Receding Horizon Control (RHC). Thereby, a prediction horizon and optimi...
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Cem Karaoguz, Andrew Dankers, Tobias Rodemann, Mark Dunn,
"An Analysis of Depth Estimation within Interaction Range",
IEEE-RSJ International Conference on Intelligent Robot and Systems (IROS 2010), 2010.
Abstract
Interactions between humans or humanoids and their environment through tasks like grasping or manipulation typically require accurate depth information. The human vision system integrates various monocular and binocular depth estimation mechanisms in order to achieve robust and reliable depth perception. Such an integrated approach can be applied to humanoid depth perception. Integration requires a knowledge of the characteristics of the methods ...
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Christian Lang, Sven Wachsmuth, Heiko Wersing, Marc Hanheide,
"Facial expressions as feedback cue in human-robot interaction - a comparison of human and automatic recognition performances",
CVPR Workshop for Human Communicative Behavior Analysis, 2010.
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Claudius Gläser, Martin Heckmann, Frank Joublin, Christian Goerick,
"Combining Auditory Preprocessing and Bayesian Estimation for Robust Formant Tracking",
IEEE Transactions on Audio, Speech, and Language Processing, vol. 18, no. 2, pp. 224–236, 2010.
Abstract
We present a framework for estimating formant trajectories. Its focus is to achieve high robustness in noisy environments. Our approach combines a preprocessing based on functional principles of the human auditory system and a probabilistic tracking scheme. For enhancing the formant structure in spectrograms we use a Gammatone filterbank, a spectral preemphasis, as well as a spectral filtering using Difference-of-Gaussians (DoG) operators. Finall...
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