ワンショット学習によるダブルショットラテアート：動的運動プリミティブを用いた協働ロボットの注ぎスキル適応
One-Shot Learning for Double-Shot Latte Art: Adapting Cobot Pouring Skills with Dynamic Movement Primitives
バリスタの1回の実演を5つのフェーズに分割し、各フェーズを独立したデカルト動的運動プリミティブで符号化することで、ラテアートを再現するワンショット学習システムを提案。カップサイズの変更にも再実演なしで適応できる。
著者: Zain Khan, Victor Hernandez Moreno, Dinh Tung Le
分類: cs.RO
原文アブストラクト
Latte art is a complex task requiring dexterity and precision, making it a challenging and commercially relevant task. A one-shot learning system is employed in which a single barista demonstration is segmented into five functionally distinct phases, each encoded as an independent Cartesian Dynamic Movement Primitive (CDMP) with its own parameters. Against a single-CDMP baseline, per-phase encoding reduces transition endpoint error by 3.2-4.5 times and art-stroke shape error by 2.7 times, reproducing jug tilt to below 1 degree RMS in the pouring phases. The learnt pour scales from a 280mL to a 400mL cup through a single spatial offset and a scaled recipe, with no re-demonstration. The phase-based CDMP approach generalises beyond latte art to any demonstration task whose phases have distinct spatial or temporal requirements whilst demonstrating the ability to adapt reproductions to suit different task requirements.