{"ID":2858955,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.07417","arxiv_id":"2510.07417","title":"FLEET: Formal Language-Grounded Scheduling for Heterogeneous Robot Teams","abstract":"Coordinating heterogeneous robot teams from free-form natural-language instructions is hard. Language-only planners struggle with long-horizon coordination and hallucination, while purely formal methods require closed-world models. We present FLEET, a hybrid decentralized framework that turns language into optimized multi-robot schedules. An LLM front-end produces (i) a task graph with durations and precedence and (ii) a capability-aware robot--task fitness matrix; a formal back-end solves a makespan-minimization problem while the underlying robots execute their free-form subtasks with agentic closed-loop control. Across multiple free-form language-guided autonomy coordination benchmarks, FLEET improves success over state of the art generative planners on two-agent teams across heterogeneous tasks. Ablations show that mixed integer linear programming (MILP) primarily improves temporal structure, while LLM-derived fitness is decisive for capability-coupled tasks; together they deliver the highest overall performance. We demonstrate the translation to real world challenges with hardware trials using a pair of quadruped robots with disjoint capabilities.","short_abstract":"Coordinating heterogeneous robot teams from free-form natural-language instructions is hard. Language-only planners struggle with long-horizon coordination and hallucination, while purely formal methods require closed-world models. We present FLEET, a hybrid decentralized framework that turns language into optimized mu...","url_abs":"https://arxiv.org/abs/2510.07417","url_pdf":"https://arxiv.org/pdf/2510.07417v1","authors":"[\"Corban Rivera\",\"Grayson Byrd\",\"Meghan Booker\",\"Bethany Kemp\",\"Allison Gaines\",\"Emma Holmes\",\"James Uplinger\",\"Celso M de Melo\",\"David Handelman\"]","published":"2025-10-08T18:15:32Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Large Language Model\"]","has_code":false}