Pursuit Movement of Pan-Tilt Camera by Feedback-Error-Learning

We investigate the ability of feedback-error-learning (FEL) algorithm through applying it to training a pan-tilt camera head for pursuing a moving target. Although the task of pursuit eye movement is re BLOCKINective and the control has a relatively large delay, our experiments show that the camera head can successfully acquire the skill of pursuit eye movement. Our experimental results also show that the performance of the learning highly depends on the gain parameter of the feedback controller module. The system sometimes oscillates or breaks down when we use the optimal feedback controller (large gain in general), because of over-training-like p h e n o m-ena. If the gain is too small, the performance is not improved by learning, though the system is stable. Those results suggest the large possibility of FEL algorithm as well as the importance of the design of feedback module.