Title: Learning sparse relational transition models. Abstract: We present a representation for describing transition models in complex uncertain domains using relational rules.  For any action, a rule selects a set of relevant objects and computes a distribution over properties of just those objects in the resulting state given their properties in the previous state.  An iterative greedy algorithm is used to construct a set of deictic references that determine which objects are relevant in any given state.   Feed-forward neural networks are used to learn the transition distribution on the relevant objects' properties.  This strategy is demonstrated to be both more versatile and more sample efficient than learning a monolithic transition model in a simulated domain in which a robot pushes stacks of objects on a cluttered table.