RT-Safe: Benchmarking Agent Safety in Real-Time Embodied Environment
Rapid progress in AI agents has brought growing attention to agent safety, with extensive evaluation focused on digital environments. As agents move into the physical world, embodied safety becomes increasingly important: failures can cause human injury and costly hardware damage. Beyond selecting safe actions, embodied agents must also operate under real-time constraints: the physical world does not pause while an agent reasons. As pedestrians move and vehicles approach during inference, an action that appears safe at observation time may become unsafe before execution. Real-time embodied safety therefore depends on both decision quality and decision latency. We introduce RT-SAFE, a simu...
| Authors | Tianruo Rose Xu, Jiawei Ren, Yichi Yang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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