Abstract :
In conventional studies, it is considered that differences of actuators are not important for realizing controllers for robots, 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 muscle like viscosity and elasticity play an important role in controlling their bodies. 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 acquired in evolution. To discuss this hypothesis, we employ a two-link manipulator, and evolve the manipulator in simulation. The task of the manipulator is to catch a ball, and the manipulator learns timing to catch. 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 body image of the manipulator has consisted of the viscosity and elasticity, and the body image has reduced learning time.
