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.
@article{arxiv.2207.02442,
title = {Transformers are Adaptable Task Planners},
author = {Vidhi Jain and Yixin Lin and Eric Undersander and Yonatan Bisk and Akshara Rai},
journal= {arXiv preprint arXiv:2207.02442},
year = {2022}
}