The ability to discover objects from raw videos and to predict their future dynamics is crucial for achieving general intelligence. While existing methods accomplish these two tasks separately, i.e., learning object segmentation with fixed dynamics or learning dynamics with known system states, we explore the feasibility of jointly accomplishing the two together in a self-supervised setting for physical environments. Critically, we show on real video datasets that learning object dynamics improves the accuracy of discovering dynamical objects.