RAIN enables robotic task generalization through region-aware interactions
Original titleRAIN: Robotic Task Generalization with Region-Aware Interaction Networks
- Outlet
- arXiv
- Published
- Importance
- 2/5
Summary
Researchers propose RAIN, which frames tasks as sequences of goal-conditioned subtasks specified by action type and target-region masks. On the new LIBERO-Analogy benchmark and real-world tabletop tasks, RAIN generalizes more robustly than existing vision-language-action baselines by reusing skills across different objects and scene layouts.
Original excerpt
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.