Planning algorithms break complex problems into sequential steps for robots. Recent work employs Large Language Models (LLMs) to generate robot policies directly from natural language in simulation and real‐world settings. Models such as GPT‐5 generalize to unseen tasks but often hallucinate because they lack sufficient environmental grounding; supplying state feedback improves robustness. We introduce a task‐planning method that uses two LLMs—one for high‐level planning and one for low‐level control—thereby raisi…