EgoSafetyBench: A Diagnostic Egocentric Video Benchmark for Evaluating Embodied VLMs as Runtime Safety Guards
Vision-language models (VLMs) are increasingly proposed as runtime safety guards for embodied agents in homes and factories. A deployable guard must catch genuinely unsafe situations while avoiding unnecessary intervention on routine but superficially alarming activity, a distinction obscured by binary safety benchmarks. We introduce EgoSafetyBench, an egocentric video benchmark of 1,200 robot-view scenarios annotated at half-second granularity, with two evaluation tracks. The situational track (800 scenarios) spans routine, safe-but-suspicious, obvious-hazard, and contextual-hazard scenes. The visual-channel track (400 scenarios) tests whether misleading in-scene text corrupts physical-s...
| Authors | Siddhant Panpatil, Arth Singh, Mijin Koo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 June 2026 |
| Last updated at source | 4 October 2026 |
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