JUICER: ロボット組立のためのデータ効率の良い模倣学習
JUICER: Data-Efficient Imitation Learning for Robotic Assembly
少数の人間の実演データから、精密で長期的な組立作業を模倣学習で実現するパイプラインを提案し、シミュレーション上の家具組立タスクで有効性を示した。
著者: Lars Ankile, Anthony Simeonov, Idan Shenfeld, Pulkit Agrawal
分類: cs.RO, cs.LG
原文アブストラクト
While learning from demonstrations is powerful for acquiring visuomotor policies, high-performance imitation without large demonstration datasets remains challenging for tasks requiring precise, long-horizon manipulation. This paper proposes a pipeline for improving imitation learning performance with a small human demonstration budget. We apply our approach to assembly tasks that require precisely grasping, reorienting, and inserting multiple parts over long horizons and multiple task phases. Our pipeline combines expressive policy architectures and various techniques for dataset expansion and simulation-based data augmentation. These help expand dataset support and supervise the model with locally corrective actions near bottleneck regions requiring high precision. We demonstrate our pipeline on four furniture assembly tasks in simulation, enabling a manipulator to assemble up to five parts over nearly 2500 time steps directly from RGB images, outperforming imitation and data augmentation baselines. Project website: https://imitation-juicer.github.io/.
