Imagining Recovery: Inference-Time Counterfactual Realignment for Vision-Language-Action Models
Vision-language-action (VLA) models have improved the flexibility and generality of robotic manipulation, yet they remain fragile to online disruptions, such as changes in task goal, scene configuration, or robot state. Existing recovery methods often require failure data, policy retraining, or external corrective agents, introducing additional data requirements and execution risks. We propose Counterfactual Realignment (CoRe), a training-free framework that recovers a frozen VLA at inference time without failure data. Upon detecting a deviation, CoRe imagines how the policy would continue toward the current goal from a recent viable state, using synthesized observations in place of physi...
| Authors | Yanyan Zhang, Disheng Liu, Kai Ye et al. |
|---|---|
| Primary category | cs.RO |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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