A quadruped stops reciting command lists: one plain-language goal becomes checkable code, and open models reach about 82% success
Instead of drawing on a fixed command library, a quadruped exploring unknown space hands the user's high-level intention to a large language model that writes an executable program restricted to a robot function library, while a vision-language model supplies open-ended semantic labels and geometric and semantic thresholds trigger replanning. In Gazebo simulation, Qwen3.5 and Kimi-K2.5 reach about 82% overall success and GPT-5-nano about 46%; finite tasks hold up well, while open-ended tasks vary more.