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Chai T.,Northeastern University China | Jin Y.,University of Surrey | Sendhoff B.,Honda Research Institute Europe
IEEE Computational Intelligence Magazine

The papers in this special section focus on the challenges and future direction of evolutionary complex engineering. © 2005-2012 IEEE. Source

Rodemann T.,Honda Research Institute Europe
IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings

The position of a sound source is an important information for robotic systems to be extracted from a sound. Of the three spherical coordinates (azimuth, elevation, distance) only the azimuth direction is extracted in most robot audition systems. So far rarely investigated is the issue of estimating the distance between robot and sound source. In this article we describe a study on distance estimation using a binaural robot system in an indoor environment for sounds ranging in distance from 0.5 to 6m. We investigated several proposed audio cues like interaural differences (IID and ITD), sound amplitude, and spectral characteristics. All cues are computed within the framework of audio proto objects. In an extensive experimental setup with more than 10000 sounds we found that both mean signal amplitude and binaural cues can, under certain circumstances, provide a very reliable distance estimation. There was no observable effect of frequency dependent attenuation so that the spectral amplitude cue was only slightly above chance level. We also investigated the loss of precision of azimuth estimation with distance. In contrast to what could be expected, the performance does not severely deteriorate when the system is calibrated for different distances. ©2010 IEEE. Source

Lucke J.,Goethe University Frankfurt | Eggert J.,Honda Research Institute Europe
Journal of Machine Learning Research

We show how a preselection of hidden variables can be used to efficiently train generative models with binary hidden variables. The approach is based on Expectation Maximization (EM) and uses an efficiently computable approximation to the sufficient statistics of a given model. The computational cost to compute the sufficient statistics is strongly reduced by selecting, for each data point, the relevant hidden causes. The approximation is applicable to a wide range of generative models and provides an interpretation of the benefits of preselection in terms of a variational EM approximation. To empirically show that the method maximizes the data likelihood, it is applied to different types of generative models including: a version of non-negative matrix factorization (NMF), a model for non-linear component extraction (MCA), and a linear generative model similar to sparse coding. The derived algorithms are applied to both artificial and realistic data, and are compared to other models in the literature. We find that the training scheme can reduce computational costs by orders of magnitude and allows for a reliable extraction of hidden causes. © 2010 Jorg Liicke and Julian Eggert. Source

Kober J.,Bielefeld University | Kober J.,Honda Research Institute Europe | Bagnell J.A.,Carnegie Mellon University | Peters J.,Empirical | Peters J.,TU Darmstadt
International Journal of Robotics Research

Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research. © The Author(s) 2013. Source

Rodemann T.,Honda Research Institute Europe
IEEE 15th International Conference on Advanced Robotics: New Boundaries for Robotics, ICAR 2011

The ability to localize a sound source is very important in interaction scenarios where the robot has to face the speaker. It is known that the horizontal position of a sound source can be easily estimated using only two microphones, however, the elevation is more difficult to determine in such a configuration. To deal with these problems the use of special outer ears (so called pinnae) has been proposed in order to allow the use of spectral cues for elevation estimation. Here we compare two algorithms that can extract spectral cues for arbitrary ear shapes and are able to localize a broad class of sounds under challenging real-world conditions. The algorithms run in real-time and are implemented on a real robot-head. © 2011 IEEE. Source

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