Luiz Marcos Garcia Gonçalves

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In this work we propose two behavioraly active policies for attentional control. These policies must act based on a multi-modal sensory feedback. Two approaches are used to derive the policies: the first one follows a simple straightforward strategy and the second one uses Q-learning to learn a policy based on the perceptual state of the system. As(More)
— We propose a new approach to reduce and abstract visual data useful for robotics applications. Basically, a moving fovea in combination with a multi-resolution representation is created from a pair of input images given by a stereo head, that reduces hundreds of times the amount of information from the original images. With this new theoretical approach(More)
In this work we propose a new approach for fast visualization and exploration of virtual worlds based on the use of cartographic concepts and techniques. Versions of cartographic maps with different levels of details can be created by using a set of operations named cartographic generalization. Car-tographic generalization employs twelve operators and(More)
In this work we propose an enhanced model for mapping from sonar sensors and odometry that allows a robot to represent an environment map in a more suitable way to both the sonar sensory data and odometry system of the robot. We use a stochastic modelling of the errors that brings up reliable information. As a contribution, we obtain a final map that is(More)
We present mechanisms for attention control and pattern categorization as the basis for robot cognition. For attention, we gather information from attentional feature maps extracted from sensory data constructing salience maps to decide where to foveate. For iden-tiication, multi-feature maps are used as input to an associative memory, allowing the system(More)
This work describes a framework for control of attention and for pattern categorization using a robot platform consisting of an articulated stereo-head with four degrees of freedom (pan, tilt, left verge, and right verge). As a practical result of this work, the system can select a region of interest, perform attention shifts involving saccadic movements,(More)
Stereo matching is an open problem in computer vision, for which local features are extracted to identify corresponding points in pairs of images. The results are heavily dependent on the initial steps. We apply image decomposition in multiresolution levels, for reducing the search space, computational time, and errors. We propose a solution to the problem(More)