データ効率の良い産業マニピュレーションのためのビデオ・アクションモデル

原文アブストラクト

Industrial manipulation requires precise alignment and controlled handling of rigid, deformable, and flexible components. Yet, the benefits of pretrained visuomotor policies remain underexplored in settings where limited access to specialized hardware, components, and fixtures constrains demonstration collection and evaluation. Understanding task-specific data requirements is important for assessing their scalability across diverse processes on the factory floor. We investigate whether video pretraining can support reliable industrial manipulation with limited action data. We propose FLUX-mimic, a video-action model, motivated by the potential of video pretraining to capture motion and interaction patterns that enable sample-efficient training. FLUX-mimic is built on a backbone pretrained on large-scale web and industrial task data, and an action expert to generate robot trajectories. We train our policy by joint video-action training. Across four automotive manufacturing benchmark tasks and two bimanual robot platforms, FLUX-mimic outperforms all considered baselines on the dexterous robot platform and matches pi_{0.5}'s average success rate on the gripper-based robot platform. On a deformable part kitting task, FLUX-mimic reaches 77.5% success with as little as two hours of task-specific action-labeled demonstrations.