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Michael Gienger, Mark Toussaint, Christian Goerick , "Whole-body Motion Planning – Building Blocks for Intelligent Systems", Motion Planning for Humanoid Robots, Springer, issue 1st Edition, 2010.
AbstractHumanoid robots have become increasingly sophisticated, both in terms of their movement as well as their sensorial capabilities. This allows one to target for more challenging problems, eventually leading to robotic systems that can perform useful tasks in everyday environments. In this paper, we review some elements we consider to be important for a movement control and planning architecture. We first explain the whole-body control concept, whic...
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Stephan Hasler , "Learning Features for Robust Object Recognition", Honda Research Institute Europe GmbH / Bielefeld University, 2010.
AbstractHumans can easily recognize a very large number of previously seen objects. Despite extensive efforts in recent years, the principles underlying this capability are hardly understood and modern object recognition systems are far from reaching human performance. The main reason for this is that the visual appearance of an object is strongly influenced by various conditions. So depending on the viewing angle different parts of an object are vis...
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Andreas Knoblauch , "Bimodal structural plasticity can explain the spacing effect in long-term memory tasks.", Frontiers in Systems Neuroscience. Conference Abstract: Computational and Systems Neuroscience, 2010.
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Matthias Rolf, Jochen Steil, Michael Gienger , "Goal Babbling permits direct learning of inverse kinematics", IEEE Transactions on Autonomous Mental Development, vol. 2, no. 3, pp. 216 - 229, 2010.
AbstractWe present an approach to learn inverse kinematics of redundant systems without prior- or expert-knowledge. The method allows for an iterative bootstrapping and refinement of the inverse kinematics estimate. The essential novelty lies in a path based sampling approach: we generate trainig data along pathes, which result from execution of the currently learned estimate along a desired path towards a goal. The information structure thereby induced ...
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Jörg Lücke and Julian Eggert , "Expectation truncation and the benefits of preselection in training generative models", JMLR, vol. 11, pp. 2855-2900, 2010.
AbstractWe 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 applica...
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Alexander Gepperth, Sven Rebhan, Stephan Hasler, Jannik Fritsch , "Biased competition in visual processing hierarchies: a learning approach using multiple cues", Cognitive Computation, 2010.
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Nils Einecke and Julian Eggert , "Evaluation of Direct Plane Fitting for Depth and Parameter Estimation", Proceedings of the International Conference on Digital Image Computing: Techniques and Applications, DICTA 2010, 2010.
AbstractRecently, a model-based depth estimation technique has been proposed, which estimates surface model parameters by means of Hooke-Jeeves optimization. Assuming a parametric surface model, the parameters best explaining the perspective changes of the surface between different views are estimated. This constitutes a fitting of models directly into stereo images, which is in contrast to the usual approach of fitting models into pre-processed disparit...
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Katrin Lohan, Sebastian Gieselmann, Anna-Lisa Vollmer, Katharina Rohlfing, Britta Wrede, Jannik Fritsch , "Does embodiment affect tutoring behavior?", Proc. IEEE 9th Int Development and Learning (ICDL) Conf, 2010.
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Miranda Grahl, Frank Joublin, Franz Kummert , "A method for visual model learning during tracking", Int. Conf. on Neural Information Processing (ICONIP), 2010.
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Volker Willert and Julian Eggert , "Dynamic Visual Motion Estimation", Probabilistic Methods for Motion Extraction (tentative), 2010.
AbstractVisual motion is the projection of scene movements on a visual sensor. It is a rich source of information for the analysis of a visual scene. Especially for dynamic vision systems the estimation of visual motion is important because it allows to deduce the motion of objects as well as the self-motion of the system relative to the environment. Therefore, visual motion serves as a basic information for navigation and exploration tasks, like obstacl...
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