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)
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)
— 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)
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)
1 Introduction In this work, vision and touch (artiicial) senses are integrated in a cooperative active system. Multi-modal sensory information acquired on-line is used by a robotic agent to perform real-time tasks involving categorization of objects. The visual-touch system proposed is able to foveate (verge) the eyes onto an object, to move the arms to(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)
One of the most important goals of bioinformatics is the ability to identify genes in uncharacterized DNA sequences. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. In this paper, we present an empirical comparison of machine learning techniques such as Naive Bayes, Decision Trees,(More)