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Rückert EA, Neumann G, Toussaint M, Maass W. Learned graphical models for probabilistic planning provide a new class of movement primitives. Frontiers in Computational Neuroscience (Special Issue on Modularity in motor control: from muscle synergies to cognitive action representation) [Internet]. 2013 ;6. Available from: http://www.frontiersin.org/computational_neuroscience/10.3389/fncom.2012.00097/abstract
Rückert EA, d'Avella A. Learned Muscle Synergies as Prior in Dynamical Systems for Controlling Bio-mechanical and Robotic Systems. In: Abstracts of Neural Control of Movement Conference (NCM 2013). Abstracts of Neural Control of Movement Conference (NCM 2013). ; 2013. Available from: http://eprints.pascal-network.org/archive/00009898/
Rückert E, d'Avella A. Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems. Frontiers in Computational Neuroscience (Special Issue on Modularity in motor control: from muscle synergies to cognitive action representation) [Internet]. 2013 ;7:138. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797962/
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Neumann G. Variational Inference for Policy Search in Changing Situations. In: Getoor L, Scheffer T Proceedings of the 28th International Conference on Machine Learning (ICML-11). Proceedings of the 28th International Conference on Machine Learning (ICML-11). New York, NY, USA: ACM; 2011. pp. 817–824. Available from: http://www.igi.tugraz.at/gerhard/research/papers/441_icmlNeumann.pdf

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