# GPT-Policy

This work introduces GPT-Policy, a framework that connects an off-the-shelf general-purpose vision-language model (VLM) to robot tools through a shared context-to-action closed loop, and evaluates its in-context learning on real robots across five context families (human videos, robot videos with actions, goal images, self-interaction history, and online human-robot interaction), finding that task-relevant context can improve success while reducing decisions and execution time, yet better task understanding does not ensure precise contact, reliable outcome verification, or physical safety.

This work introduces GPT-Policy, a framework that connects an off-the-shelf general-purpose vision-language model (VLM) to robot tools through a shared context-to-action closed loop, and evaluates its in-context learning on real robots across five context families (human videos, robot videos with actions, goal images, self-interaction history, and online human-robot interaction), finding that task-relevant context can improve success while reducing decisions and execution time, yet better task understanding does not ensure precise contact, reliable outcome verification, or physical safety.