We present a bio-inspired control method for moving-target seeking with a mobile robot, which resembles a predator-prey scenario. The motor repertoire of a simulated Khepera robot was restricted to a discrete number of "gaits". After an exploration phase, the robot automatically synthesizes a model of its motor repertoire, acquiring a forward model. Two additional components were introduced for the task of catching a prey robot. First, an inverse model to the forward model, which is used to determine the action (gait) needed to reach a desired location. Second, while hunting the prey, a model of the prey's behavior is learned online by the hunter robot. All the models are learned ab initio, without assumptions, work in egocentric coordinates, and are probabilistic in nature. Our architecture can be applied to robots with any physical constraints (or embodiment), such as legged robots.