Abstract
In conventional studies, it is considered that differences of actuators are not important for realizing intelligent behavior, so, usually conventional robots employ motors as actuators. On the other hand, animals have muscles, and recently, it is reported that physical properties of the muscles like viscosity and elasticity play an important role in realizing intelligent behavior. In this paper, we consider that learning time (time required for adapting themselves to the environment) works as selection pressure, and actuators like muscles are ac quired in evolution. To discuss this hypothesis, we employ a 2-link manipulator, and evolve the manipulator in simulation. The tasks of the manipulator are to avoid an obstacle and to catch a ball. Fitness of the manipulator is calculated from learning time. Simulations have been conducted, and as a result, manipulators that have actuators with adequate viscosity and elasticity have been obtained. By analyzing the result, we have found that the viscosity and the elasticity have reduced learning time.
