EgoSafetyBench: A Diagnostic Egocentric Video Benchmark for Evaluating Embodied VLMs as Runtime Safety Guards
arXiv:2607.00218v1 Announce Type: cross Abstract: Vision-language models (VLMs) are now 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 that binary safety benchmarks obscure. We introduce EgoSafetyBench, an egocentric video benchmark of 1,200 robot-view scenarios annotated at half-second granularity, to evaluate VLMs as streaming guards across two tracks. The situational track (800 scenarios) spans four fam
The proliferation of advanced vision-language models (VLMs) and the increasing deployment of embodied AI agents in real-world settings necessitates robust safety evaluation benchmarks.
Evaluating embodied VLMs for safety is crucial for their reliable deployment, particularly in critical applications within homes and factories, where false positives or negatives can have significant consequences.
The introduction of EgoSafetyBench provides a specialized, fine-grained benchmark that moves beyond binary safety assessments, allowing for more nuanced and accurate evaluations of VLM performance as safety guards.
- · AI Safety Researchers
- · Embodied AI Developers
- · Robotics Manufacturers
- · Humanoid Robotics Developers
- · VLM Developers with poor safety architectures
- · Companies relying on simplistic safety evaluations
Improved safety and reliability of embodied AI systems using VLMs as runtime guards.
Accelerated development and adoption of embodied AI in sensitive environments like factories and homes.
Increased public trust and regulatory acceptance of autonomous agents in everyday life, driven by demonstrated safety capabilities.
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Read at arXiv cs.AI