用大模型+强化学习让机器人更懂指令、反应更快。
Hybrid Framework for Robotic Manipulation: Integrating Reinforcement Learning and Large Language Models
- 大模型负责理解指令，强化学习控制机械臂动作
- 任务完成时间减少33.5%，准确率和适应性分别提升18.1%与36.4%
- 适合需要自然语言交互的智能机器人研发
本文提出一种融合强化学习（RL）与大语言模型（LLMs）的混合框架，用于提升机器人操作任务性能。通过将强化学习用于精确的底层控制，大语言模型用于高层任务规划与自然语言理解，该框架实现了机器人系统中低层执行与高层推理的有效衔接。机器人可理解并执行复杂的人类指令，并实时适应环境变化。在基于PyBullet的仿真环境中，使用Franka Emika Panda机械臂进行测试，涵盖多种操作场景。结果表明，相比仅使用强化学习的系统，该框架使任务完成时间减少33.5%，准确率提升18.1%，适应性提高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，使其更具效率、灵活性与人机交互能力。未来研究将聚焦于仿真到现实的迁移、可扩展性及多机器人系统，以进一步拓展该框架的应用范围。
原文摘要 · Abstract (English)
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.
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