Multi-Agent Systems for Robotic Autonomy with LLMs
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913828229349376 |
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| author | Chen, Junhong Yang, Ziqi Xu, Haoyuan G Zhang, Dandan Mylonas, George |
| author_facet | Chen, Junhong Yang, Ziqi Xu, Haoyuan G Zhang, Dandan Mylonas, George |
| contents | Since the advent of Large Language Models (LLMs), various research based on such models have maintained significant academic attention and impact, especially in AI and robotics. In this paper, we propose a multi-agent framework with LLMs to construct an integrated system for robotic task analysis, mechanical design, and path generation. The framework includes three core agents: Task Analyst, Robot Designer, and Reinforcement Learning Designer. Outputs are formatted as multimodal results, such as code files or technical reports, for stronger understandability and usability. To evaluate generalizability comparatively, we conducted experiments with models from both GPT and DeepSeek. Results demonstrate that the proposed system can design feasible robots with control strategies when appropriate task inputs are provided, exhibiting substantial potential for enhancing the efficiency and accessibility of robotic system development in research and industrial applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05762 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Agent Systems for Robotic Autonomy with LLMs Chen, Junhong Yang, Ziqi Xu, Haoyuan G Zhang, Dandan Mylonas, George Robotics Artificial Intelligence Since the advent of Large Language Models (LLMs), various research based on such models have maintained significant academic attention and impact, especially in AI and robotics. In this paper, we propose a multi-agent framework with LLMs to construct an integrated system for robotic task analysis, mechanical design, and path generation. The framework includes three core agents: Task Analyst, Robot Designer, and Reinforcement Learning Designer. Outputs are formatted as multimodal results, such as code files or technical reports, for stronger understandability and usability. To evaluate generalizability comparatively, we conducted experiments with models from both GPT and DeepSeek. Results demonstrate that the proposed system can design feasible robots with control strategies when appropriate task inputs are provided, exhibiting substantial potential for enhancing the efficiency and accessibility of robotic system development in research and industrial applications. |
| title | Multi-Agent Systems for Robotic Autonomy with LLMs |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2505.05762 |