Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 7 pot

... on reinforcement learning and a neural network”, in Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002, vol. 3, pp. 2 672 –2 677 . 5 D. Zhou and K. H. Low, “Combined ... 17 M. J. L. Boada, R. Barber, and M. A. Salichs, “Visual approach skill for a mobile robot using learning and fusion of simple skills”, Robotics and Autonomous Syst...

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... 20 0 93 91,5 87, 5 84 71 29 15 90 88,5 86 78 63 18,5 30 86 84 79 ,5 73 45,5 11,5 45 82 78 ,5 73 ,5 60 25,5 0 60 77 72 56,5 32 12,5 0 75 65,5 52,5 37 16 0 0 1.6.1 Robot Localization Inside ... global and/ or local object specific detail. Global details do not have a precise geometric location in- side the object, such as statistics of gray levels or colors, textures, e...

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... optical flow, the algorithms can be classified into three main categories: gradient-based methods [71 ], match- ing techniques [72 ], and frequency-based approaches [73 ]. But a recent re- view [74 ] ... images and that used the space-variant form of the derivative operator to calculate the image gradi- ents was reported in [77 , 75 ]. It is hypothesized that the outline of classical...

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... α-shape of a finite point set S is a polytope uniquely determined by S and the param- eter α that controls the level-of-detail. A subset T ⊆ S of size |T | = k +1with 0 ≤ k ≤ 2 belongs to a set ... region-growing method, projection method, and sculpting-based method. In the next sub-sections we will ex- plain each of these categories. 6.4.1 Volumetric Methods The volumetric method consists...

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... micro-facets where each facet is assumed to be one side of a symmetric V-cavity. The set of micro-facets has a mean normal vector of n, and a random variable α is used to represent the angle between each ... generation and optimal triangulation. Technical Report P9 2-0 00 47, Xerox Palo Alto Research Center, 1992. [7] F. Bernardini, J. Mittleman, H. Rushmeier, C. Silva, and G. Taubin...

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... of 3-D parameters from calibrated sets of 2-D parameters is computationally very efficient, requiring only a small fraction of computational power as compared to the low-level segmentation algorithms[5]. ... Motion Understanding the observation process is significantly non-trivial. Video images of the hu- man body are extremely high-dimensional signals and the mapping between body pose and...

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... Barber, R., Salichs, M.A., (2002) “Self-generation by a mo- bile robot of topological maps of corridors”. IEEE International Con- ference on Robotics and Automation, pp. 266 2-2 6 67. 17 Escalera A. ... Luo, R. C.; Potlapalli, H., (1994) “Landmark recognition using projec- tion learning for mobile robot navigation”. IEEE International Confer- ence on Neural Networks, vol. 4, pp....

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... inertial and visual mechanisms in the stabilization of gaze in natural and artificial systems, From: Motion Vision Computational, Neural, and Echological Con- straints, Eds. J. M. Zanker and ... human visual system : functional and topological transformation performed in the periferal visual field”, Biological Cyber- netics, vol. 44, 1982. 102 Y. Sun et al. model of the rob...

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... Length (m) )( ˆ mL error )( ˆ mL error 0.93 0.0290 -9 6% 1.0 278 10.5% 1.14 0.128 -8 9.3% 1.061 -7 .0% 1.31 0.1213 -9 0% 1.415 7. 9% 1.46 0.1 577 -8 9% 1.50 2.6% 4 Continuous Reinforcement Learning Algorithm for Skills Learning in an ... upon the robot& apos;s actuators [ 17] . Bonasso et al. [18] define skills as the robot s connection with the world. For Chati...

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