Titlebook: Advances in Robot Learning; 8th European Workhop Jeremy Wyatt,John Demiris Conference proceedings 2000 Springer-Verlag Berlin Heidelberg 20

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How Does a Robot Find Redundancy by Itself?,with respect to the given task dynamically. The controller is derived based on a least-mean-square method. A simulation result of a camera-manipulator system is shown to demonstrate that the proposed method can find redundancy automatically.

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Map Building through Self-Organisation for Robot Navigation,robot navigation at Manchester University, using mechanisms of self-organisation (artificial neural networks) to identify perceptual landmarks in the robot’s environment, and to use such landmarks for route learning and self-localisation, as well as the quantitative assessment of the performance of such systems.

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Kwangcheol Shin,Ajith Abraham,Sang Yong Hanand non-Markovian domains, and present a direct approach to managing estimator variance, the ccBeta algorithm. Empirical results in an autonomous robotics domain are also presented showing improved performance using the new ccBeta method.

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https://doi.org/10.1007/11751595es between all the different types on comparing the resulting maps. In the case of comparing occupancy labels, no differences were found between the following pairs of methods: RATE and SUM (. ? . = 0.157), ELFES and RATE (. ? . = 0.600) and ELFES and SUM (. ? . = 0.593).

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