Every home is different, and every person likes things done in their own way. Therefore, home robots of the future need to both reason about the sequential nature of day-to-day tasks and generalize to user’s preferences. To this end, we propose a Transformer Task Planner (TTP) that learns high-level reasoning from demonstrations by leveraging object attribute-based representations. TTP is pretrained on multiple preferences in a simulated dishwasher loading task and shows generalization to unseen preferences using a single demonstration as a prompt. Further, we demonstrate real-world dish rearrangement using TTP with a Franka robotic arm.