The integration of large language models (LLMs) in robotics research has shown potential in bridging the gap between abstract high-level planning and detailed robotic control. However, challenges remain in translating these models’ sophisticated language processing capabilities into actionable control strategies, particularly in dynamic environments. To address this, researchers have introduced the Plan-Seq-Learn (PSL) framework, which integrates LLM-based planning to guide reinforcement learning policies in solving long-horizon robotic tasks.