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Kuppuswamy N, Carbajal JP. Learning a curvature dynamic model of an octopus-inspired soft robot arm using flexure sensors. In: The European Future Technologies Conference and Exhibition. The European Future Technologies Conference and Exhibition. ; 2011. Available from: http://www.sciencedirect.com/science/article/pii/S1877050911006065
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
Kohen D, Karklinsky M, Meirovitch Y, Flash T, Shmuelof L. Shortening preparation time for curved trajectories reveals an ongoing control of movement segments. In: Neural control of movement (NCM 2014). Neural control of movement (NCM 2014). Amsterdam, Netherlands; 2014.
Klampfl S, Maass W. Emergence of Dynamic Memory Traces in Cortical Microcircuit Models through STDP. The Journal of Neuroscience [Internet]. 2013 ;33:11515-11529. Available from: http://www.jneurosci.org/content/33/28/11515.abstract
Kindermans P-J, wyffels F, Caluwaerts K, Guns B, Schrauwen B. Towards incorporation of hierarchical Bayesian models into evolution strategies for quadruped gait generation. In: Proceedings of the 21st Belgian-Dutch Conference on Machine Learning. Proceedings of the 21st Belgian-Dutch Conference on Machine Learning. ; 2012. Available from: https://biblio.ugent.be/publication/2134106/file/2134110.pdf
Kim S, Billard A. Estimating the non-linear dynamics of free-flying objects. Robotics and Autonomous Systems. 2012 ;60:1108–-1122.
Kim S, Gribovskaya E, Billard A. Learning motion dynamics to catch a moving object. In: 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids). 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids). Nashville, TN; 2010. pp. 106-111. Available from: http://www.academia.edu/download/30230251/humanoids2010_ready.pdf
Kim S, Billard A. Estimating the non-linear dynamics of free-flying objects. Robotics and Autonomous Systems. 2012 :–.
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
Khansari-Zadeh SM, Billard A. A dynamical system approach to realtime obstacle avoidance. Autonomous Robots [Internet]. 2012 ;32:433-454. Available from: http://dx.doi.org/10.1007/s10514-012-9287-y
Khansari-Zadeh SM, Billard A. Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models. Robotics, IEEE Transactions on [Internet]. 2011 ;27:943 -957. Available from: http://lasa.epfl.ch/publications/publications.php
Khansari-Zadeh SM, Kronander K, Billard A. Learning to Play Minigolf: A Dynamical System-based Approach. Advanced Robotics [Internet]. 2012 . Available from: http://infoscience.epfl.ch/record/181052/files/MiniGolf_AR12.pdf
Khansari-Zadeh SMohammad, Billard A. Realtime Avoidance of Fast Moving Objects: A Dynamical System-based Approach. In: Electronic proc. of the Workshop on Robot Motion Planning: Online, Reactive, and in Real-Time, Int. Conf. on Intelligent Robots and Systems (IROS). Electronic proc. of the Workshop on Robot Motion Planning: Online, Reactive, and in Real-Time, Int. Conf. on Intelligent Robots and Systems (IROS). ; 2012. Available from: http://www.reflexxes.com/iros2012ws/Paper_04.pdf
Khansari-Zadeh SM, Billard A. Imitation Learning of Globally Stable Non-Linear Point-to-Point Robot Motions using Nonlinear Programming. In: Proceeding of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Proceeding of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). ; 2010. pp. 2676-2683. Available from: http://homepages.inf.ed.ac.uk/s0565544/0146.pdf
Khansari-Zadeh SMohammad, Billard A. Learning Control Lyapunov Function to Ensure Stability of Dynamical System-based Robot Reaching Motions. Robotics and Autonomous Systems. 2014 .
Khansari M, Lemme A, Meirovitch Y, Schrauwen B, Giese MA, Steil J, Ijspeert AJ, Billard A. Workshop on Benchmarking of State-of-the-Art algorithms in Generating Human-Like Robot Reaching Motions. IEEE Humanoids. 2013 .
Karklinsky M, Flash T. The 2/3 power law originates in the motor plan: a motor imagery study. In: Neural control of movement (NCM 2014). Neural control of movement (NCM 2014). Amsterdam, Netherlands; 2013.
Karklinsky M, Flash T. The 2/3 power law originates in the motor plan. In: Computational motor Control workshop 9. Computational motor Control workshop 9. Beer-sheva, Isreal.; 2013.
Kappel D, Nessler B, Maass W. STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning. PLoS Computational Biology. 2014 .

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