We propose a method for autonomously learning an object-centric representation of a high-dimensional environment that is suitable for planning. Such abstractions can be immediately transferred between tasks that share the same types of objects, resulting in agents that require fewer samples to learn a model of a new task. We demonstrate our approach on a series of Minecraft tasks to learn object-centric representations - directly from pixel data - that can be leveraged to quickly solve new tasks. The resulting learned representations enable the use of a task-level planner, resulting in an agent capable of forming complex, long-term plans.