Publications

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2014
Giese MA. Biological and Body Motion Perception. The Oxford Handbook of Perceptual Organisation (Forthcoming) [Internet]. 2014 . Available from: http://www.oxfordhandbooks.com/10.1093/oxfordhb/9780199686858.001.0001/oxfordhb-9780199686858-e-008
2013
Galtier M, Wainrib G. A biological gradient descent for prediction through a combination of STDP and homeostatic plasticity. Neural Computation [Internet]. 2013 ;25(11). Available from: http://de.arxiv.org/abs/1206.4812
D K, Karklinsky M, Flash T, Shmuelof L. The building blocks of curved trajectories: studying the effect of shortened preparation time on movement planning and execution. In: ISFN. ISFN. Eilat, Israel; 2013.
2012
Emmerich C, Reinhart RF, Steil JJ. Balancing of neural contributions for multi-modal hidden state association. In: European Symposium on Artificial Neural Networks. European Symposium on Artificial Neural Networks. Brugge: d-facto; 2012.
2011
Hauser H, Neumann G, Ijspeert A, Maass W. Biologically inspired kinematic synergies enable linear balance control of a humanoid robot. Biological Cybernetics [Internet]. 2011 ;104:235–249. Available from: http://www.springerlink.com/content/5217485124776363/
Kormushev P, Ugurlu B, Calinon S, Tsagarakis NG, Caldwell DG. Bipedal Walking Energy Minimization by Reinforcement Learning with Evolving Policy Paramerization. In: International Conference on Intelligent Robots and Systems (IROS). International Conference on Intelligent Robots and Systems (IROS). San Francisco, California; 2011. Available from: http://programming-by-demonstration.org/papers/Kormushev-IROS2011.pdf
Caluwaerts K, Schrauwen B. The body as a reservoir: locomotion and sensing with linear feedback. In: 2nd International Conference on Morphological Computation. 2nd International Conference on Morphological Computation. Venice, Italy; 2011. Available from: https://biblio.ugent.be/publication/1203118/file/1900813.pdf
2010
Khansari-Zadeh SM, Billard A. BM: An Iterative Algorithm to Learn Stable Non-Linear Dynamical Systems with Gaussian Mixture Models. In: Proceeding of the International Conference on Robotics and Automation (ICRA). Proceeding of the International Conference on Robotics and Automation (ICRA). ; 2010. pp. 2381-2388. Available from: http://www.researchgate.net/publication/224156439_BM_An_iterative_algorithm_to_learn_stable_non-linear_dynamical_systems_with_Gaussian_mixture_models/file/9c960517160383ff2c.pdf

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