Explicitly task oriented probabilistic active vision for a mobile robot

A mobile robot has always uncertainty about the world model. Reducing this uncertainty is very hard because there is a huge amount of information and the robot must focus on the most relevant one. The selection of the most relevant information must be based on the task the robot is executing, but there could be several sources of information where the robot would like to focus on. This is also true in robot soccer where the robot must pay attention to landmarks in order to self-localize and to the ball and robots in order to follow the status of the game. In the presented work, an explicitly task oriented probabilistic active vision system is proposed. The system tries to minimize the most relevant components of the uncertainty for the task that is been performed and it is explicitly task oriented in the sense that it explicitly considers a task specific value function. As a result, the system estimates the convenience of looking towards each of the available objects. As a test-bed for the presented active vision approach, we selected a robot soccer attention problem: goal covering by a goalie player.