Exploiting interaction between sensory morphology and learning

This paper proposes a system that automatically designs the sensory morphology of an autonomous robot. This system uses two kinds of adaptation, ontogenetic adaptation and phylogenetic adaptation, to optimize the sensory morphology of the robot. In ontogenetic adaptation, individuals with many different sensory morphologies use reinforcement learning to adapt to a task. In phylogenetic adaptation, a Genetic Algorithm is used to select morphologies with which the robot can learn the task faster. We made the system design a line-following robot, and carried out experiments to compare the design solution with a hand-coded design. The results have shown that the designed robot outperforms the hand-coded design in terms of line-following accuracy and learning speed, although it has fewer sensors than hand-coded robots. The paper also shows the effective use of sensory morphology obtained by our system.