  We propose to make the physical characteristics of a robot oscillate while it
learns to improve its behavioral performance. We consider quantities such as
mass, actuator strength, and size that are usually fixed in a robot, and show
that when those quantities oscillate at the beginning of the learning process
on a simulated 2D soft robot, the performance on a locomotion task can be
significantly improved. We investigate the dynamics of the phenomenon and
conclude that in our case, surprisingly, a high-frequency oscillation with a
large amplitude for a large portion of the learning duration leads to the
highest performance benefits. Furthermore, we show that morphological wobbling
significantly increases exploration of the search space.
