This paper introduces a new hybrid framework that combines Reinforcement Learning (RL) and Large Language Models (LLMs) to improve robotic manipulation tasks. By utilizing RL for accurate low-level control and LLMs for high level task planning and understanding of natural language, the proposed framework effectively connects low-level execution with high-level reasoning in robotic systems. This integration allows robots to understand and carry out complex, human-like instructions while adapting to changing environments in real time. The framework is tested in a PyBullet-based simulation environment using the Franka Emika Panda robotic arm, with various manipulation scenarios as benchmarks. The results show a 33.5% decrease in task completion time and enhancements of 18.1% and 36.4% in accuracy and adaptability, respectively, when compared to systems that use only RL. These results underscore the potential of LLM-enhanced robotic systems for practical applications, making them more efficient, adaptable, and capable of interacting with humans. Future research will aim to explore sim-to-real transfer, scalability, and multi-robot systems to further broaden the framework's applicability.

翻译：本文提出了一种结合强化学习（RL）与大语言模型（LLMs）的新型混合框架，以提升机器人操作任务性能。通过利用强化学习实现精确的低层控制，并借助大语言模型完成高层任务规划与自然语言理解，所提框架有效连接了机器人系统中的低层执行与高层推理。这种集成使机器人能够理解并执行复杂类人指令，同时实时适应环境变化。该框架在基于PyBullet的仿真环境中以Franka Emika Panda机械臂为平台，通过多种操作场景进行基准测试。结果表明，与仅使用强化学习的系统相比，任务完成时间缩短33.5%，准确率与适应性分别提升18.1%和36.4%。这些结果凸显了大语言模型增强型机器人系统在实际应用中的潜力，使其更高效、适应性强且具备人机交互能力。未来研究将聚焦于仿真到现实迁移、可扩展性及多机器人系统，以进一步拓展该框架的适用性。