Predictive vision-language monitoring for proactive safety in robot task execution

Robots that execute language-conditioned tasks in dynamic environments often rely on feedback only after an action has failed, which can be insufficient when failures involve collisions or workspace conflicts. This paper presents a predictive monitoring framework that uses Vision-Language Models (VLMs) to assess near-future execution risk during robot task execution. The framework first generates structured plans with action execution conditions and a plan-level fallback action. During execution

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ethics.ai (3 August 2026), “Predictive vision-language monitoring for proactive safety in robot task execution,” evidence record 15714, https://ethics.ai/record/15714 (originally published by Frontiers in Robotics and AI).

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