  Every home is different, and every person likes things done in their
particular 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
actions from demonstrations by leveraging object attribute-based
representations. TTP can be pre-trained on multiple preferences and shows
generalization to unseen preferences using a single demonstration as a prompt
in a simulated dishwasher loading task. Further, we demonstrate real-world dish
rearrangement using TTP with a Franka Panda robotic arm, prompted using a
single human demonstration.
