Learning gaits for the Stiquito

Abstract

It has been shown that the use of cyclic genetic algorithms can be an effective means of gait generation for hexapod robot simulations. They can, with only low-level primitives, produce reasonable gaits in minimal time. In addition, their output requires little in intermediate controller complexity as it is a sequence of these primitives, which can be fed directly into the robot. In this paper, we test the applicability of these algorithms on an actual robot. A model for simulation was produced based on the measured capabilities of the Stiquito robot. This model was trained with the CGA using five random initial populations; gaits quickly evolved for all five. Tests on the actual semi-autonomous robot showed that after 1000 generations gaits comparable to the best designed by human engineers were produced.