Hybrid Framework for Robotic Manipulation: Integrating RL and LLMs for Task Planning and Execution

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Combines reinforcement learning for low-level control with LLMs for high-level task planning in robotic manipulation. The LLM decomposes natural language instructions into subtask sequences while RL policies handle the physical execution. Demonstrates the LLM-as-planner + RL-as-executor pattern that is emerging across robotics, with potential transfer to software agent architectures where planning and execution are similarly decoupled.