Transformerは適応可能なタスクプランナー
Transformers are Adaptable Task Planners
物体属性ベースの表現を用いたTransformerタスクプランナーを提案し、単一のデモンストレーションをプロンプトとして未知の好みに汎化できることを食洗機積載タスクと実機での食器整理で示した。
著者: Vidhi Jain, Yixin Lin, Eric Undersander, Yonatan Bisk, Akshara Rai
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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.
