However, to handle such optical properties and achieve basic eye movement functions, new calibration methods are needed. Therefore, two novel and online techniques are presented that in one case perform a global identification of the optical process through artificial neural techniques and in the other case compute the physical parameters by using environmental feature-tracking and controlled rotations of the cameras. Self-alignment of the cameras is also achieved using a similar technique. Markers ground task-related data on sensor data flow. Behaviors command markers by specifying weights for the different possible command parameter values.
Cooperation is achieved by combining the commands sent to the same marker.
We discuss also multi-agent cooperation and propose a scheme for learning cooperative behaviors. Markers have an important role in multi-agent cooperation and learning. We present experiments on a control system that enables a real robot to help other robots in transferring objects. The knowledge of the image-joint jacobian is not straightforward and results traditionally from calibration procedures. As far as the authors are aware, this is the first implementation of a real-time learning controller on an active vision system.