Learning to Act under Visual Interruptions with Vision-Language-Action Models
Vision-language-action (VLA) models have demonstrated strong capabilities in robotic manipulation, but they are typically developed and evaluated with all camera streams available throughout task execution. When a camera stops delivering frames during task execution, the policy must continue acting without access to subsequent observations from the missing view. Despite its practical importance, how such interruptions affect closed-loop manipulation remains insufficiently understood. To investigate this problem, we introduce MAIL-Bench, a benchmark that evaluates visual interruptions with VLA models. By interrupting different cameras at multiple stages of each policy's successful referenc...
| Authors | Mingle Jiang, Rui Xu, Yunke Wang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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