Biblio
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2011. Batch Intrinsic Plasticity for Extreme Learning Machines. Artificial Neural Networks and Machine Learning – ICANN 2011. Pt. 1. 6791:339–346.
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2011. A constrained regularization approach for input-driven recurrent neural networks. Differential Equations and Dynamical Systems. 19:27–46.
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2011. Editorial Special Corner on Cognitive Robotics. Cognitive Processing. 12:317–318.
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2011. Integrating Feature Maps and Competitive Layer Architectures For Motion Segmentation. Neurocomputing. 74:1372–1381.
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2011. Learning Lab - Physical Interaction with Humanoid Robots for Pupils. Proc. Robotics in Education. :21–28.
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2011. Manual Intelligence as a Rosetta Stone for Robot Cognition. Robotics Research. 66:135–146.
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2011. Neural learning and dynamical selection of redundant solutions for inverse kinematic control. Proc. IEEE Int. Conf. Humanoid Robots. :564–569.
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2011. Online Goal Babbling for rapid bootstrapping of inverse models in high dimensions. IEEE Int. Conf. Development and Learning and on Epigenetic Robotics (best student paper award). 2:1–8.
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2011. Reservoir regularization stabilizes learning of Echo State Networks with output feedback. Proc. European Symposium on Artificial Neural Networks. :59–64.
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2011. Robot with automatic selection of task-specific representations for imitation learning. European Patent Office.
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2011. State prediction: a constructive method to program recurrent neural networks. Artificial Neural Networks and Machine Learning – ICANN 2011 : 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I. 6791:159–166.
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2011. What do humanoid robots offer to experimental psychology ? Connectionist models of neurocognition and emergent behavior : from theory to applications ; proceedings of the 12th Neural Computation and Psychology Workshop, Birkbeck, University of London, 8 - 10 April 2010. 20:361–371.
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2010. Bootstrapping inverse Kinematics with Goal Babbling. :147–154.
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2010. Efficient online learning of a non-negative sparse autoencoder. European Symposium Artificial Neural Networks. :1–6.
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2010. Figure-ground segmentation using metrics adaptation in levelset methods. European Symposium on Artificial Neural Networks. :417–422.
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2010. Goal Babbling permits direct learning of inverse kinematics. IEEE Trans. Autonomous Mental Development. 2:216–229.
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2010. Human-Robot Interaction for Learning and Adaptation of Object Movements. IEEE Int. Conf. Intelligent Robots and Systems. :4901–4907.
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2010. Imitating object movement skills with robots — A task-level approach exploiting generalization and invariance. The IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems : Conference Proceedings. :1262–1269.
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2010. Interactive Learning of Inverse Kinematics with Nullspace Constraints using Recurrent Neural Networks. Proc. 20. Workshop on Computational Intelligence.
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2010. Learning Flexible Full Body Kinematics for Humanoid Tool Use. Int. Symp. Learning and Adaptive Behavior in Robotic Systems (Best Paper Award). :171–176.
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2010. Learning Inverse Kinematics for Pose-Constraint Bi-Manual Movements. From Animals to Animats 11. 11th International Conference on Simulation of Adaptive Behavior, SAB 2010. Proceedings. 6226
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2010. Mastering Growth while Bootstrapping Sensorimotor Coordination. Int. Conf. on Epigenetic Robotics.
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2010. Mit Kopf, Körper und Hand: Herausforderungen Humanoider Roboter. Automatisierungstechnik, special issue on "humnoid robotics". 58:630–638.
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2010. Neural competition for motion segmentation. 18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). :59–64.
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2010. OOP: Object-Oriented-Priority for Motion Saliency Maps. Workshop on Brain Inspired Cognitive Systems. :370–381.
