RT-Safe: Benchmarking Agent Safety in Real-Time Embodied Environment

Key points

• Rapid progress in AI agents has brought growing attention to agent safety, with extensive evaluation focused on digital environments.1
• As agents move into the physical world, embodied safety becomes increasingly important: failures can cause human injury and costly hardware damage.1
• Beyond selecting safe actions, embodied agents must also operate under real-time constraints: the physical world does not pause while an agent reasons.1
• Finally, we show that RT-SAFE can support offline RL training and substantially reduce collision rates while achieving strong task completion.1

Sources (1)

• [1]RT-Safe: Benchmarking Agent Safety in Real-Time Embodied Environment
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 01:57 AM

  We introduce RT-SAFE, a simulated urban benchmark for evaluating embodied-agent safety under real-time constraints.

  Rapid progress in AI agents has brought growing attention to agent safety, with extensive evaluation focused on digital environments.

Extractive summary: sentences quoted from the sources.