arXiv:2603.06001v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models enable robots to perform manipulation tasks directly from natural language instructions and are increasingly viewed as a foundation for generalist robotic policies. However, their reliability under Out-of-Distribution (OOD) instructions remains underexplored. In this paper, we reveal a critical failure mode in which VLA policies continue executing visually plausible actions even when the language instruction contradicts the scene. We refer to this phenomenon as linguistic blindness, where VLA policies
The rapid development and deployment of VLA models in robotics necessitates robust evaluation of their real-world reliability, especially under unexpected conditions.
This research identifies a critical vulnerability in VLA models, linguistic blindness, that could severely limit their deployment in sensitive or safety-critical robotic applications.
The understanding of VLA model limitations is deepened, highlighting the need for more robust grounding techniques before widespread adoption of generalist robotic policies.
- · AI safety researchers
- · Robotics companies focusing on robust AI
- · Developers of attention recalibration techniques
- · Developers of ungrounded VLA models
- · Companies relying on naive VLA model deployment
Further research and development will focus on integrating train-free attention recalibration and similar grounding techniques into VLA architectures.
Improved VLA model reliability will accelerate the adoption of generalist robotic policies in controlled environments, moving towards more complex real-world tasks.
The enhanced trustworthiness of VLA models could lead to new regulatory frameworks for autonomous robotic systems, emphasizing linguistic grounding and OOD robustness.
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Read at arXiv cs.AI