報酬設計なしでマルチモーダルな接触を伴う技能を模倣学習する
Learning Multimodal Contact-Rich Skills from Demonstrations Without Reward Engineering
ロボットが掃除・筆記・皮むきなどの接触を伴う日常タスクを、報酬関数を手設計せずに実演から学べるマルチモーダル模倣学習フレームワークを提案し、実機Sawyerで高い成功率を示した。
著者: Mythra V. Balakuntala, Upinder Kaur, Xin Ma, Juan Wachs, Richard M. Voyles
分類: cs.RO
原文アブストラクト
Everyday contact-rich tasks, such as peeling, cleaning, and writing, demand multimodal perception for effective and precise task execution. However, these present a novel challenge to robots as they lack the ability to combine these multimodal stimuli for performing contact-rich tasks. Learning-based methods have attempted to model multi-modal contact-rich tasks, but they often require extensive training examples and task-specific reward functions which limits their practicality and scope. Hence, we propose a generalizable model-free learning-from-demonstration framework for robots to learn contact-rich skills without explicit reward engineering. We present a novel multi-modal sensor data representation which improves the learning performance for contact-rich skills. We performed training and experiments using the real-life Sawyer robot for three everyday contact-rich skills -- cleaning, writing, and peeling. Notably, the framework achieves a success rate of 100\% for the peeling and writing skill, and 80\% for the cleaning skill. Hence, this skill learning framework can be extended for learning other physical manipulation skills.
