This research introduces LDSC, a framework that combines Large Language Models with hierarchical reinforcement learning to improve robots' ability to learn complex tasks.

• Leverages LLMs for intelligent subgoal selection and option reuse
• Significantly enhances sample efficiency for reinforcement learning
• Improves generalization and multi-task adaptability
• Demonstrates promising results for complex robotic applications

This breakthrough matters for engineering as it addresses fundamental challenges in robotic learning: exploration inefficiency and high computational demands. The approach enables robots to learn complex behaviors more efficiently by using language models to guide the learning process.

Option Discovery Using LLM-guided Semantic Hierarchical Reinforcement Learning