Hybrid Framework for Robotic Manipulation: Integrating Reinforcement Learning and Large Language Models

TL;DR

This paper presents a hybrid framework combining Reinforcement Learning and Large Language Models to enhance robotic manipulation, enabling robots to understand complex instructions and adapt in real-time within simulated environments.

Contribution

The novel integration of RL and LLMs for robotic manipulation improves task efficiency, accuracy, and adaptability over RL-only systems.

Findings

33.5% decrease in task completion time

18.1% improvement in accuracy

36.4% enhancement in adaptability

Abstract

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.