RAIN: Robotic Task Generalization with Region-Aware Interaction Networks
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...
| Authors | Seunghun Lee, Kyuhwan Shim, Yeogyeong Song et al. |
|---|---|
| Primary category | cs.RO |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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