Staged learning of saccadic eye movements with a robot camera head

In motor learning, two main problems arise: the missing teacher signal, and the necessity to explore high-dimensional sensorimotor spaces. Several solutions have been proposed, all of them limited in some respect. In the present work, an alternative learning mechanism is developed for the example of saccade control, implemented on a stereo vision robot camera head. This approach relies on two main principles: averaging over imperfect learning examples, and learning in multiple stages. In each stage, a saccade controller is trained with a set of imperfect learning examples. Afterwards, the output of this controller serves as starting point for the creation of a new training set with better quality. By the repetition of these steps, the controllers' performance can be incrementally improved without the need to search from scratch for the rare learning examples with very good quality.