Abstract: Interactive Evolutionary Algorithms (IEAs) use human input to help drive a search process. Traditionally, IEAs allow the user to exhibit preferences among some set of individuals. Here we present a system in which the user directly demonstrates what he or she prefers. Demonstration has an advantage over preferences because the user can provide the system with a solution that would never have been presented to a user who can only provide preferences. However, demonstration exacerbates the user fatigue problem because it is more taxing than exhibiting preferences. The system compensates for this by retaining and reusing the user demonstration, similar in spirit to user modeling. The system is exercised on a robot locomotion and obstacle avoidance task that has an obvious local optimum. The user demonstration is provided through low-level control. The system is compared against a high-level fitness function that is susceptible to becoming trapped by a local optimum and a mid-level fitness function designed to remove the local optimum. We show that our proposed system outperforms most variants of these completely automatic methods, providing further evidence that Evolutionary Robotics (ER) can benefit by combining the intuitions of inexpert human users with the search capabilities of computers.
Abstract: Transferring designs in evolutionary robotics from simulation to reality remains problematic. It has been addressed by using quasi-static physics simulators, adding noise to encourage robustness, and evolving primarily in simulation then evolving on actual hardware for fine-tuning. This paper experiments with this idea: All physics simulators have errors, but if the errors are distinct, one might profitably use multiple simulators to detect unrealistic physical behavior in simulation. Two physics simulators are used to evolve a controller for quadruped locomotion. Preliminary results validate some assumptions and further work is suggested.