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In this paper we present a new methodology for robot learning that combines ideas from statistical generalization and reinforcement learning. First we apply statistical generalization to compute an approximation for the optimal control policy as defined by training movements that solve the given task in a number of specific situations. This way we obtain a(More)
Intelligent robots cannot be programmed in advance for all possible situations, but they should be able to generalize based on the acquired knowledge. In robot learning based on imitation of human activity we often use statistical methods that generalize observed (learned) movements. The acquired data is used to generate useful robots responses in(More)
This research investigates the determinants of the capital structure of small and medium enterprises (SMEs) using a unique database that includes over 19,000 Brazilian firms and spans 13 years of data. The econometric analysis employs the System Generalized Method of Moments estimator (GMM-Sys) and two strong results emerge: (a) profitability is negatively(More)
Imitation learning has been proposed as the basis for fast and efficient acquisition of new sensorimotor behaviors. Movement representations such as dynamic movement primitives were designed to enable the reproduction of the demonstrated behaviors and their modulation with respect to unexpected external perturbations. Various statistical methods were(More)
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