Learning adaptive leg cycles using fitness biasing

Abstract

This paper discusses the use of fitness biasing to alter the control of a seven-microprocessor robot as it shifts from one environment to another. The robot was initially using a gait evolved to work on a smooth surface (tile). When tested on a rough surface (carpet) the learned gait was found to be inappropriate because the legs were causing drag as they repositioned. An efficient move to reposition on the smooth surface did not work on the rough surface. Anytime learning with fitness biasing was applied to the continued evolution of the individual leg cycles as the simulated robot moved from an area of smooth to rough terrain. An actual robot was used to test the results. Following training using fitness biasing, the robot's gait was more appropriate for a rough surface as it learned to raise its leg more before initiating the return movement.