Online automatic code generation for robot swarms: LLMs and self-organizing hierarchy
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911219275792384 |
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| author | Zhu, Weixu Dorigo, Marco Heinrich, Mary Katherine |
| author_facet | Zhu, Weixu Dorigo, Marco Heinrich, Mary Katherine |
| contents | Our recently introduced self-organizing nervous system (SoNS) provides robot swarms with 1) ease of behavior design and 2) global estimation of the swarm configuration and its collective environment, facilitating the implementation of online automatic code generation for robot swarms. In a demonstration with 6 real robots and simulation trials with >30 robots, we show that when a SoNS-enhanced robot swarm gets stuck, it can automatically solicit and run code generated by an external LLM on the fly, completing its mission with an 85% success rate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04774 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Online automatic code generation for robot swarms: LLMs and self-organizing hierarchy Zhu, Weixu Dorigo, Marco Heinrich, Mary Katherine Robotics Artificial Intelligence Multiagent Systems Our recently introduced self-organizing nervous system (SoNS) provides robot swarms with 1) ease of behavior design and 2) global estimation of the swarm configuration and its collective environment, facilitating the implementation of online automatic code generation for robot swarms. In a demonstration with 6 real robots and simulation trials with >30 robots, we show that when a SoNS-enhanced robot swarm gets stuck, it can automatically solicit and run code generated by an external LLM on the fly, completing its mission with an 85% success rate. |
| title | Online automatic code generation for robot swarms: LLMs and self-organizing hierarchy |
| topic | Robotics Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2510.04774 |