[2603.30022] Hybrid Framework for Robotic Manipulation: Integrating Reinforcement Learning and Large Language Models

Computer Science > Robotics arXiv:2603.30022 (cs) [Submitted on 31 Mar 2026] Title:Hybrid Framework for Robotic Manipulation: Integrating Reinforcement Learning and Large Language Models Authors:Md Saad, Sajjad Hussain, Mohd Suhaib View a PDF of the paper titled Hybrid Framework for Robotic Manipulation: Integrating Reinforcement Learning and Large Language Models, by Md Saad and 2 other authors View PDF HTML (experimental) 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. Fu...