Generalization and Transfer in Robot Control

We address the generalization and transfer of sensorimotor programs in robot systems. We use a factorable control-based approach that provides a natural, discrete abstraction of the underlying continuous state/action space and thus allows for the application of learning algorithms that converge in practical amounts of time. We argue that our approach provides an efficient means for the adaptation of skills to new situations. We show the performance gains for our framework in simulation, and demonstrate results from on-line learning on a bimanual robot.