Matthew Howard, Stefan Klanke, Michael Gienger, Christian Goerick, Sethu Vijayakumar,
"Behaviour Generation in Humanoids by Learning Potential-based Policies from Constrained Motion",
Applied Bionics and Biomechanics, vol. - Special Issue on Humanoid Robots, 2009.
Abstract
Movement generation that is consistent with observed or demonstrated behaviour is an efficient way to seed movement planning in complex, high dimensional movement systems like humanoid robots. We present a method for learning potential-based policies from constrained motion data. In contrast to previous approaches to direct policy learning, our method can combine observations from a variety of contexts where different constraints are in force, to...
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Stephan Hasler, Heiko Wersing, Stephan Kirstein, Edgar Körner,
"Large-Scale Real-Time Object Identification Based on Analytic Features",
Artificial Neural Networks – ICANN 2009, pp. 663–672, 2009.
Abstract
Inspired by biological findings, we present a system that is able to robustly identify a large number of pre-trained objects in real-time. In contrast to related work, we do not restrict the objects' pose to characteristic views but rotate them freely in hand in front of a cluttered background. We describe the essential system's ingredients, like prototype-based figure-ground segmentation, extraction of brain-like analytic features, and a simple ...
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Anna-Lisa Vollmer, Kathrin Lohan, Kerstin Fischer, Yukie Nagai, Karola Pitsch, Jannik Fritsch, Katharina Rohlfing, Britta Wrede,
"People Modify Their Tutoring Behavior in Robot-Directed Interaction for Action Learning",
International Conference on Development and Learning, 2009.
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Thomas Weisswange, Constantin Rothkopf, Tobias Rodemann, Jochen Triesch,
"A Reinforcement learning model develops causal inference and cue integration abilities",
Proceedings of the 2009 Bernstein Conference on Computational Neuroscience, Frankfurt, 2009.
Abstract
In recent years it has been suggested that the performance of human subjects in a large variety of perceptual tasks can be modelled using Bayesian inference (e.g. [1]). The success of these methods stems from their capacity to explicitly represent the involved uncertainties. Recently, such methods have been extended to the task of model selection where the observer not only has to integrate different cues into a single estimate, but needs to firs...
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Inna Mikhailova,
"Internal control for autonomous open-ended acquisition of new behaviors",
Technische Fakultaet der Universitaet Bielefeld, 2009.
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Andreas Knoblauch, Marc-Oliver Gewaltig, Ursula Körner, Edgar Körner,
"Structural plasticity and memory: Catastrophic forgetting, amnesia, and the spacing effect.",
Frontiers in Systems Neuroscience. Conference Abstract: Computational and systems neuroscience, 2009.
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Yaochu Jin, Robin Gruna, Ingo Paenke, Bernhard Sendhoff,
"Evolutionary multi-objective optimization of robustness and innovation in redundant genetic representations",
2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (IEEE MCDM 2009), pp. 38–45, 2009.
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Lars Gräning,
"Knowledge Extraction from Aerodynamic Design Data and its Application to 3D Turbine Blade Geometries",
International Workshop on Machine Learning for Aerospace, 2009.
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Niko Wilbert, Robert Legenstein, Mathias Franzius, Laurenz Wiskott,
"Reinforcement learning on complex visual stimuli",
BMC Neuroscience, vol. 10, no. Suppl 1, pp. P90, 2009.
Abstract
Animals are confronted with the problem of initiating motor actions based on very complex sensor y input. We have build a biologically plausible model that uses reinforcement learning on complex visual stimuli to direct an agent towards a target. This is made possible by first extracting a high-level representation of the scene with a hierarchical network and then applying a correlation based reinforcement learning rule....
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Ben Jones,
"The Evolutionary Emergence of Neural Organisation in Computational Models of Primitive Organisms",
University of Birmingham, 2009.
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