Some robotic tasks require an accurate control to follow the desired trajectory in the presence of unforeseen external disturbances and system parameters variations. In this case conventional control techniques such as PID must be constantly readjusted and a compromise solution must be adopted. This problem can be avoided using a learning process that automatically learns the appropriate control law and adapts to ongoing system variations. But a drawback of many learning systems is that they are not effective for non-toy problems. In this paper we present the results obtained with a categorization and learning algorithm able to perform efficient generalization of the observed situations, and learn accurate control policies in a short time without any previous knowledge of the plant.

This work was supported by the project 'Sistema reconfigurable para la navegación basada en visión de robots caminantes y rodantes en entornos naturales.' (00).

European Conference on Artificial Intelligence (ECAI), 2004, Valencia (España)

Peer Reviewed