Fitness biasing to produce adaptive gaits for hexapod robots

Anytime learning with fitness biasing was shown in an earlier work to be an effective tool for learning leg cycles for a hexapod robot. This learning system was capable of adapting to changes in the environment. Although the leg cycles were appropriate for rougher terrain, the gaits produced with them by a standard genetic algorithm were not capable of bearing the robot´s load. In this paper, we present the use of anytime learning with fitness biasing to improve the gaits produced by allowing the learning system to adapt to unforeseen changes in the environment and the robot´s capabilities. Training and tests were done in simulation, with the resultant gaits tested on the actual robot.