Hybrid Framework for Robotic Manipulation: Integrating Reinforcement Learning and Large Language Models
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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918420837040128 |
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| author | Saad, Md Hussain, Sajjad Suhaib, Mohd |
| author_facet | Saad, Md Hussain, Sajjad Suhaib, Mohd |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_30022 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hybrid Framework for Robotic Manipulation: Integrating Reinforcement Learning and Large Language Models Saad, Md Hussain, Sajjad Suhaib, Mohd Robotics Artificial Intelligence 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. |
| title | Hybrid Framework for Robotic Manipulation: Integrating Reinforcement Learning and Large Language Models |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2603.30022 |