Hybrid Framework for Robotic Manipulation: Integrating Reinforcement Learning and Large Language Models
This addresses the problem of making robotic systems more efficient and adaptable for human interaction, though it is incremental as it combines existing methods.
This paper tackled the problem of robotic manipulation by integrating Reinforcement Learning (RL) for low-level control and Large Language Models (LLMs) for high-level task planning, resulting in a 33.5% decrease in task completion time and improvements of 18.1% in accuracy and 36.4% in adaptability compared to RL-only systems.
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.