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Christian Lang, Marc Hanheide, Manja Lohse, Heiko Wersing, Gerhard Sagerer , "Feedback Interpretation based on Facial Expressions in Human-Robot Interaction", Proc. IEEE RO-MAN, 2009.
AbstractIn everyday conversation besides speech people also communicate by means of nonverbal cues. Facial expressions are one important cue, as they can provide useful information about the conversation, for instance whether the interlocutor seems to understand or appears to be puzzled. Similarly, in human–robot interaction they also give feedback about the interaction situation. We present a Wizard of Oz user study in an object teaching scenario where ...
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Martijn Meeter, Rob Veldkampf, Yaochu Jin , "Multiple memory stores and operant conditioning: A rationale for memory's complexity", Brain and Cognition, vol. 69, no. 1, pp. 200–208, 2009.
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Florian Röhrbein, Julian Eggert, Edgar Körner , "Child-friendly divorcing: Incremental Hierarchy Learning in Bayesian Networks", Proceedings of the 2009 International Joint Conference on Neural Networks (IJCNN), 2009.
AbstractThe autonomous learning of concept hierarchies is still a matter of research. Here we present a learning schema for Bayesian networks which results in a nested structure of sub- and superclass relationships. It is based on so-called parent divorcing but exploits the similarity of all nodes involved as expressed by their connectivity pattern. If the procedure is applied to simple object-property pairings a nested taxonomic hierarchy emerges. We fu...
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Benjamin Dittes, Alexander Gepperth, Antonello Ceravola, Jannik Fritsch, Christian Goerick , "Self-management for neural dynamics in brain-like information processing", Proceedings of the 6th International Conference on Autonomic Computing and Communications, 2009.
AbstractNeural dynamics coupled by adaptive synaptic information transmission provide a very powerful tool for biologically inspired visual processing systems[4]. Currently, progress is limited by the computing time needed to evaluate the underlying equations and by the high number of parameters necessary to tune to achieve the desired system performance. In this contribution we apply Autonomic Computing techniques to overcome these limitations. We appro...
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Miguel Vaz , "Developmentally inspired computational framework for embodied speech imitation", Universidade do Minho, Escola de Engenharia, 2009.
AbstractThis thesis is concerned with the autonomous acquisition of speech production skills by a robotic system. The acquisition should occur in interaction with a human tutor, making little or no assumptions on the vocabulary and language of interaction. A particular target embodiment of the acquisition framework presented in this thesis is the humanoid robot Asimo. Because of its size, and the little knowledge of the world it possesses, a child’s voic...
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Yaochu Jin, Robin Gruna, Bernhard Sendhoff , "Pareto Analysis of Evolutionary and Learning Systems", Frontiers of Computer Science in China, vol. 3, no. 1, pp. 4–17, 2009.
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Martin Heracles, Bram Bolder, Christian Goerick , "Fast Detection of Arbitrary Planar Surfaces from Unreliable 3D Data", International Conference on Intelligent Robots and Systems (IROS), 2009.
AbstractMan-made real-world environments are dominated by planar surfaces many of which constitute behaviorrelevant entities. Thus, the ability to perceive planar surfaces is vital for any embodied system operating in such environments, be it human or robotic. In this paper, we present an architecture for detection and estimation of planar surfaces in the scene from calibrated stereo images. They are represented in a behavior-oriented way, focusing on ge...
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Katrin Lohan, Anna-Lisa Vollmer, Jannik Fritsch, Katharina Rohlfing, Britta Wrede , "Which ostensive stimuli can be used for a robot to detect and maintain tutoring situations?", Int. Workshop on Social Signals Processing, 2009.
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Matthew Howard, Stefan Klanke, Michael Gienger, Christian Goerick, Sethu Vijayakumar , "A Novel Method for Learning Policies from Variable Constraint Data", Autonomous Robots, 2009.
AbstractMany everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment.Constraints are usually unobservable and frequently change between contexts. In this paper, we present a novel approach for learning (unconstrained) control policies from movement data, where observations come from movements under different constraints. As a key ingredient, we introduce a small but highly effective modificat...
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Michael Garcia Ortiz and Alexander Gepperth , "Local self-adaptation mechanisms for large-scale neural system building", Proceedings of the 2nd International Conference on Cognitive Neurodynamics, 2009.
AbstractFor integrating neural networks into large systems, dynamical stability and parameter settings are key issues, especially for popular recurrent network models such as dynamic neural fields. In neural circuits, homeostatic plasticity seems to counter these problems. Here we present a set of gradient adaptation rules that autonomously regulate the strength of synaptic input and the parameters of the transfer function for each neuron individually. B...
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