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RoboSafe: Safeguarding Embodied Agents via Executable Safety Logic

본 논문은 최근 급부상하고 있는 시각‑언어 모델(VLM) 기반 구현 에이전트가 실제 물리 환경에서 수행되는 복잡한 작업을 안전하게 수행하도록 보장하는 방법론을 제시한다. 기존 연구들은 주로 정적 규칙 기반 필터링이나 프롬프트 엔지니어링을 통해 위험 명령을 차단하려 했지만, 이러한 접근법은 두 가지 근본적인 한계를 가진다. 첫째, 정적 규칙은 사전에 정의된 위험 상황에만 대응할 수 있어, 새로운 혹은 변형된 위험 시나리오에 취약하다. 둘째, 프롬프트 수준 제어는 모델 내부의 추론 과정을 직접 관찰하거나 수정할 수 없기 때문에, 시간에

RoboSafe: Safeguarding Embodied Agents via Executable Safety Logic

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Seismology modeling agent: A smart assistant for geophysical researchers

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