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Service robots are expected to help us in different tasks in various environments such as homes, hospitals and offices. This work presents the first steps towards building a service robot at our university. The robot is provided with a topological map of a building floor (environmental map). Using this map the robot learns a path from one location to another by means of reinfocerment learning. During execution the robot is provided with a navigation map, related to the environmental map. We make use of a comercial Roomba robot and show how to create an interface in order to control it. Service robots are expected to help us in different tasks in various environments such as homes, hospitals and offices. This work presents the first steps towards building a service robot at our university. The robot is provided with a topological map of a building floor (environmental map). Using this map the robot learns a path from one location to another by means of reinfocerment learning. During execution the robot is provided with a navigation map, related to the environmental map. We make use of a comercial Roomba robot and show how to create an interface in order to control it.

In this work we present principled methods for the coordination of a robot's oculomotor system with the rest of its body motor systems. The problem is to decide which physical actions to perform next and where the robot's gaze should be directed in order to gain information that is relevant to the success of its physical actions. Previous work on this problem has shown that a reward-based coordination mechanism provides an efficient solution. However, that approach does not allow the robot to move its gaze to different parts of the scene, it considers the robot to have only one motor system, and assumes that the actions have the same duration. The main contributions of our work are to extend that previous reward-based approach by making decisions about where to fixate the robot's gaze, handling multiple motor systems, and handling actions of variable duration. We compare our approach against two common baselines: random and round robin gaze allocation. We show how our method provides a more effective strategy to allocate gaze where it is needed the most.