Your Model Already Knows: Attention-Guided Safety Filter for Vision-Language-Action Models
Vision-Language-Action (VLA) models have demonstrated impressive end-to-end performance across a variety of robotic manipulation tasks. However, these policies offer no guarantees against collisions with task-irrelevant objects in the scene. Existing safety filters sidestep this problem by querying a vision-language model (VLM) to identify obstacles and their locations. This, however, is too slow to run in the control loop and can only be invoked at episode initialization, leaving the filter unable to track moving obstacles. We discover that a small number of attention heads within a VLA model reliably localize the object the policy intends to approach. These heads can be exploited within...
| Authors | Seongbin Park, Fan Zhang, Baharan Mirzasoleiman et al. |
|---|---|
| Primary category | cs.RO |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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