MONEE: Using Parental Investment to Combine Open-Ended and Task-Driven Evolution

This paper is inspired by a vision of self-sufficient robot collectives that adapt autonomously to deal with their environment and to perform user-defined tasks at the same time. We introduce the monee algorithm as a method of combining open-ended (to deal with the environment) and task-driven (to satisfy user demands) adaptation of robot controllers through evolution. A number of experiments with simulated e-pucks serve as proof of concept and show that with monee, the robots adapt to cope with the environment and to perform multiple tasks. Our experiments indicate that monee distributes the tasks evenly over the robot collective without undue emphasis on easy tasks.