arXiv:2605.30924v1 Announce Type: new Abstract: MLLM-powered embodied agents deployed in real-world environments encounter physical hazards. However, existing approaches lack explicit mechanisms for identifying hazards and reasoning about action-conditioned risks, leading agents to either miss risky interactions or over-identify risks. To address this, we propose EMBGuard, the first MLLM-based safety guardrail for embodied agents designed to decouple physical risk reasoning from agent policy. By evaluating a (visual observation, action) pair, EMBGuard identifies hazardous configurations and pr
The increasing sophistication and deployment of MLLM-powered embodied agents necessitate robust safety mechanisms for real-world interaction, moving beyond theoretical safeguards.
Ensuring the safe operation of embodied AI agents is critical for their commercial adoption and societal integration, mitigating risks of physical harm and building public trust.
The development of explicit hazard-aware guardrails for embodied AI agents shifts safety from a reactive policy concern to a proactive, integrated system component.
- · AI Safety Researchers
- · Robotics Companies
- · Embodied AI Developers
- · Industrial Automation Sector
- · Companies with lax AI safety standards
- · Early, un-guarded embodied AI deployments
Increased public and regulatory acceptance of physically interactive AI agents.
Acceleration of general-purpose embodied AI deployment in hazardous or dynamic environments.
Reduced insurance premiums and liability for companies deploying safety-augmented embodied AI.
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Read at arXiv cs.CL