{"ID":2892471,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.16068","arxiv_id":"2507.16068","title":"Compositional Coordination for Multi-Robot Teams with Large Language Models","abstract":"Multi-robot coordination has traditionally relied on a mission-specific and expert-driven pipeline, where natural language mission descriptions are manually translated by domain experts into mathematical formulation, algorithm design, and executable code. This conventional process is labor-intensive, inaccessible to non-experts, and inflexible to changes in mission requirements. Here, we propose LAN2CB (Language to Collective Behavior), a novel framework that leverages large language models (LLMs) to streamline and generalize the multi-robot coordination pipeline. LAN2CB transforms natural language (NL) mission descriptions into executable Python code for multi-robot systems through two core modules: (1) Mission Analysis, which parses mission descriptions into behavior trees, and (2) Code Generation, which leverages the behavior tree and a structured knowledge base to generate robot control code. We further introduce a dataset of natural language mission descriptions to support development and benchmarking. Experiments in both simulation and real-world environments demonstrate that LAN2CB enables robust and flexible multi-robot coordination from natural language, significantly reducing manual engineering effort and supporting broad generalization across diverse mission types. Website: https://sites.google.com/view/lan-cb","short_abstract":"Multi-robot coordination has traditionally relied on a mission-specific and expert-driven pipeline, where natural language mission descriptions are manually translated by domain experts into mathematical formulation, algorithm design, and executable code. This conventional process is labor-intensive, inaccessible to no...","url_abs":"https://arxiv.org/abs/2507.16068","url_pdf":"https://arxiv.org/pdf/2507.16068v3","authors":"[\"Zhehui Huang\",\"Guangyao Shi\",\"Yuwei Wu\",\"Vijay Kumar\",\"Gaurav S. Sukhatme\"]","published":"2025-07-21T21:09:15Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"cs.LG\",\"cs.MA\"]","methods":"[\"Large Language Model\",\"Language Model\"]","project_urls":"[\"https://sites.google.com/view/lan-cb\"]","has_code":false,"code_links":[{"ID":611986,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2892471,"paper_url":"https://arxiv.org/abs/2507.16068","paper_title":"Compositional Coordination for Multi-Robot Teams with Large Language Models","repo_url":"https://github.com/google/safevalues","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0},{"ID":611987,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2892471,"paper_url":"https://arxiv.org/abs/2507.16068","paper_title":"Compositional Coordination for Multi-Robot Teams with Large Language Models","repo_url":"https://github.com/Zhehui-Huang/lan2cb","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}