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This paper describes experimental results based on the authors' prior-proposed scheme: learning of sensory-based, goal-directed behavior. The scheme was implemented on the mobile robot "YAMABICO" and learning of a set of goal-directed navigations were conducted. The experiment assumed that the robot receives no global information such as position nor prior environment model. Instead, the robot was trained to learn adequate maneuvering in the adopted workspace by building a correct mapping between a spatio-temporal sequence of sensory inputs and maneuvering outputs on a neural structure. The experimental results showed that sufficient training generated rigid dynamical structure of a fixed point and limit cycling in the sensory-based state space, which realized robust navigations of homing and cyclic routing even against certain changes of environment as well as miscellaneous noises in the real world.