Image recognition and reinforcement learning have become increasingly essential for robot control. With image data, we can obtain information on the position and shape of an object without attaching sensors to robots. Reinforcement learning can be applied directly to non-linear systems, which makes it applicable to a wide range of robotic systems. Model-based reinforcement learning (MBRL) uses data experienced by robots directly as supervisor for the neural network, resulting in high sample efficiency and shorter learning times. However, since MBRL depends on the supervised data, it could not perform well for the input image with noise. Therefore, our goal is to achieve robust control with MBRL by utilizing a spiking neural network (SNN) that would be robust against noise.