Silent Failures in Physical AI: A Literature Review of Runtime Action Authorization for Autonomous Systems
Physical AI systems increasingly map multimodal observations, language instructions, and learned world representations into physically consequential actions. Robotics foundation models, vision-language-action models, and world-model-based autonomous systems can condition decisions that move vehicles, robots, drones, and industrial machines. This transition exposes a safety problem that is not fully captured by conventional AI content moderation or by classical robot safety alone: a black-box model may issue a physically consequential action while appearing confident, plausible, and semantically aligned. The resulting failure can be silent, arising from sensor drift, occlusion, state-estim...
| Authors | Barak Or |
|---|---|
| Primary category | cs.RO |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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