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This last behavior is connected to an attentional selector of obstacles, which shifts attention towards regions situated close to the trajectory between the robot and the goal position. The Go to Point behavior interprets the incoming visual information as the nearest regions that could interfere in the approach to the destination position and generates changes in the robot trajectory according to this. In this situation, the focus of attention alternates between uncertainty regions and potential obstacles, keeping overt control on one of the targets and covert control on the other one.

Modeling of the second room during the exploration of the environment of figure 14. environments. As an initial approach, it is assumed that the environment is formed by rectangular rooms communicated by doors and may contain objects on the floor. The result of the modeling process is a topological graph that represents the set of rooms (nodes) and their connections (edges). Each node in this representation contains the metric description of the corresponding room model. Using this basic metric information robots do not need to maintain in parallel a metric map of the environment.

To minimize ξ, we use the Stochastic Gradient Descent (Robbins & Monro, 1951), which has proven to be an efficient method to solve similar problems (Olson, 2008). In SGD, the error function is iteratively reduced by randomly selecting a parameter and modifying it using a gradient descent step. Each step is modulated by a learning rate λ which is reduced over time to avoid local minima. Being S the set of parameters and ξ the error function, the method proceeds as follows: 1. Initialize λ 2. While not converge: (a) randomly select a parameter si of the set S (b) Compute the step of si ( si ) in the gradient descent direction of ξ according to the uncertainty of si .

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