RAIN: 領域認識型インタラクションネットワークによるロボットタスクの汎化
RAIN: Robotic Task Generalization with Region-Aware Interaction Networks
タスクを目標条件付きサブタスクの系列として定式化し、対象領域マスクとアクション種別で指定することで、学習済みスキルを新しい物体やシーン配置に再利用・組み合わせできるVLAモデルを提案した。
著者: Seunghun Lee, Kyuhwan Shim, Yeogyeong Song, Seung Hwan Kim, Sihaeng Lee, Byoung-Tak Zhang, Sunghoon Im, Gunhee Lee
分類: cs.RO, cs.CV
原文アブストラクト
Existing vision-language-action (VLA) models benefit from large-scale vision-language pretraining, but tend to memorize training trajectories, limiting their ability to adapt learned interactions to new task and scene configurations. To address this problem, we propose \textbf{Region-Aware Interaction Networks (RAIN)}, which formulates a task as a sequence of goal-conditioned subtasks, each specified by an action type and a target-region mask. This formulation allows learned skills to be reused across different objects and scene layouts and composed into task sequences not seen during training. Its Target-adaptive Cross-View Encoder (TCE) conditions multi-view features on target masks and exchanges information across views, while PlanDiT predicts target-directed trajectories and actions. A Transition Head estimates subtask completion to enable automatic transitions. We also introduce a \textbf{Reference-based Retargeting Strategy (RRS)}, which uses recorded trajectories to synthesize smooth approaches to reference interactions with alternative target regions, without collecting additional data. Experiments on \textbf{LIBERO-Analogy}, our new benchmark for robotic task generalization, and real-world tabletop tasks demonstrate that RAIN generalizes more robustly than existing VLA baselines.