視覚運動拡散ポリシー訓練のためのリアルタイム操作者介入
Real-Time Operator Takeover for Visuomotor Diffusion Policy Training
操作者が実行中の視覚運動拡散ポリシーにリアルタイムで介入し、修正動作を教示できる枠組みを提案し、剛体・変形・粒状物体タスクで性能向上を実証した。
著者: Marco Moletta, Michael C. Welle, Nils Ingelhag, Jesper Munkeby, Danica Kragic
分類: cs.RO, cs.LG
原文アブストラクト
We present a Real-Time Operator Takeover (RTOT) paradigm that enables operators to seamlessly take control of a live visuomotor diffusion policy, guiding the system back to desirable states or providing targeted corrective demonstrations. Within this framework, the operator can intervene to correct the robot's motion, after which control is smoothly returned to the policy until further intervention is needed. We evaluate the takeover framework on three tasks spanning rigid, deformable, and granular objects, and show that incorporating targeted takeover demonstrations significantly improves policy performance compared with training on an equivalent number of initial demonstrations alone. Additionally, we provide an in-depth analysis of the Mahalanobis distance as a signal for automatically identifying undesirable or out-of-distribution states during execution. Supporting materials, including videos of the initial and takeover demonstrations and all experiments, are available on the project website: https://operator-takeover.github.io/
