Probabilistic relational models are an efficient way to learn and represent the dynamics in realistic environments consisting of many objects. Autonomous intelligent agents that ground this representation for all objects need to plan in exponentially large state spaces and large sets of stochastic actions. A key insight for computational efficiency is that successful planning typically involves only a small subset of relevant objects. In this paper, we introduce a probabilistic model to represent planning with subsets of objects and provide a definition of object relevance. Our definition is sufficient to prove consistency between repeated planning in partially grounded models restricted to relevant objects and planning in the fully grounded model. We propose an algorithm that exploits object relevance to plan efficiently in complex domains. Empirical results in a simulated 3D blocksworld with an articulated manipulator and realistic physics prove the effectiveness of our approach.