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References 1. Uyen, H. S. V., J. W. Jeon. Combine Kalman Filter and Particle Filter to Improve Color Tracking Algorithm. - In: Proc. of International Conference on Control, Automation and Systems 2007, 558-561. 2. Anati, R., D. Scar amu z z a, K. G. Derpanis, K. Daniilidis. Robot Localization Using Soft Object Detection. - In: Proc. of IEEE International Conference on Robotics and Automation (ICRA’2012), 2012, 4992-4999. 3. Ivanjko, E., M. Uasak, I. Petrovic. Kalman Filter Theory Based Mobile Robot Pose Tracking Using Occupancy Grid Maps. - In: Proc. of

. A Probabilistic Image Jigsaw Puzzle Solver. - In: IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, 2011, 183-190. 18. Yang, Xingwei, N. Adluru, L. J. Latechi. Particle Filter with State Permutations for Solving Image Jigsaw Puzzles. - In: IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, 2011, 2873-2880. 19. Grundmann, M., V. Dwatra, Han Mei, I. Essa. Efficient Hierarchical Graph-Based Video Segmentation. - In: IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 2010, 2141

, 2013. 15. May, A. D. Traffic Flow Fundamentals, Transportation and Traffic Theory in the 21st Century. -26. In: ISTTT. Englewood Cliffs, NJ, Prentice-Hall, 1990. 16. Mboup, M., C. Join, M. Fliess. A Revised Look at Numerical Differentiation with an Application to Nonlinear Feedback Control. - In: 15 th Mediterrean Conference on Control and Automation (MED.07), 2007. http://hal.inria.fr/inria/00142588/fr/ 17. Mihaylova, L., R. B o e l. A Particle Filter for Freeway Traffic Estimation. - In: Proc. of 43rd IEEE Conf. on Decision and Control, 14-17 December 2004

. IEEE ITSC 2006, 17-20 September 2006, Toronto, Canada. 25. Mihaylov a, L., R. Boel. A Particle Filter for Freeway Traffic Estimation. - In: Proc. of 43rd IEEE Conf. on Decision and Control, 14-17 December 2004, Atlantis, Paradise Island, Bahamas, 2106-2111.

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method is illustrated. The Cartesian position of the CoM of the robot is defined by xcom, ycom and zcom. The reference ZMP trajectory is pref and p is the ZMP feedback signal from the Cart table model which consists of xZMP and yZMP. The objective locomotion parameters namely, lateral sway and step size are provided by RLPF for a desired walking speed 4. Reinforcement learning This section describes a recently proposed Reinforcement Learning algorithm based on Particle Filters (RLPF) for global search in policy space, which is capable of finding multiple

. 2. New Orleans: IEEE, 2004, pp. 1308-1313. N. Ceccarelli, et al., "Set membership localization and map building for mobile robots," in Systems & Control: Foundations & Applications , Part III. Boston: Birkhauser, 2006, pp. 289-308. A. Howard, "Multi-robot simultaneous localization and mapping using particle filters," The International Journal on Robotics Research , vol. 25, no. 12. Sage Science Press, 2006, pp. 1243-1256. P. Bergasa, et al., "SLAM and map merging," Journal of Physical Agents , vol. 3, no. 1, 2009. G. Dedeoglu and G. Sukhatme, "Landmark