Grasp2Twist: 強化学習による両手巧みな瓶の蓋開けの学習
Grasp2Twist: Learning Bimanual Dexterous Jar Opening by Reinforcement Learning
強化学習を用いて両手で瓶の蓋を掴んで回して開けるタスクを学習するシステムを提案し、実機へゼロショット転移で88%の成功率を達成した。
著者: Mo Xu, Yunfu Deng, Jianuo Wang, Josiah Hanna, Bilge Mutlu
分類: cs.RO
原文アブストラクト
This paper presents Grasp2Twist, a bimanual dexterous manipulation system that learns to grasp and twist open jar lids using reinforcement learning. Learning this task raises three challenges: learning a unified policy for a multi-stage task, sustaining lid twisting, and sim-to-real transfer. To address the first challenge, we introduce a continuous enclosure measure to guide grasp formation and a binary enclosure indicator to guide the grasp-to-twist transition for unified policy learning. We derive both from the geometric relationship between the object center and the convex hull formed by the hand's palm and fingertips. Kinematic constraints limit how far the hand can rotate the lid with fixed contacts, so sustained twisting requires finger contact reconfiguration. We use a three-stage curriculum to facilitate exploration of these contact changes and also improve robustness for sim-to-real transfer. With our approach, the learned policy demonstrates finger gaiting, reconfiguring hand-object contacts to sustain lid rotation. It transfers zero-shot to the physical system and achieves an 88% task success rate across six household containers, including peanut-butter, vitamin, and instant-coffee jars. Ablations further validate the roles of the geometric enclosure in grasp formation and the curriculum in contact-reconfiguration exploration.