FLEET: Formal Language-Grounded Scheduling for Heterogeneous Robot Teams

2025/10/08 by Corban G. Rivera, Rivera, Corban, Byrd, Grayson +14
Computer Science · Engineering · #AI-based Problem Solving and Planning #FOS: Computer and information sciences #Robotics (cs.RO) #Software Engineering Techniques and Practices #Systems Engineering Methodologies and Applications

paper · pdf · doi:10.48550/arxiv.2510.07417

openalex publication_date 2025/10/08 · openalex created_date 2025/10/18 · openalex updated_date 2026/07/28

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.

Citations

• Hierarchical Temporal Logic Task and Motion Planning for Multi-Robot Systems
• Automatic MILP Model Construction for Multi-Robot Task Allocation and Scheduling Based on Large Language Models
• FlowMP: Learning Motion Fields for Robot Planning with Conditional Flow Matching
• Adaptive Bi-Level Multi-Robot Task Allocation and Learning under Uncertainty with Temporal Logic Constraints
• PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks
• ConceptAgent: LLM-Driven Precondition Grounding and Tree Search for Robust Task Planning and Execution
• Generative Factor Chaining: Coordinated Manipulation with Diffusion-based Factor Graph
• TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models
• Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots
• SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models
• SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• RoCo: Dialectic Multi-Robot Collaboration with Large Language Models
• Building Cooperative Embodied Agents Modularly with Large Language Models
• Language Models Meet World Models: Embodied Experiences Enhance Language Models
• Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
• Diffusion policy: Visuomotor policy learning via action diffusion
• RT-1: Robotics Transformer for Real-World Control at Scale
• CAPE: Corrective Actions from Precondition Errors using Large Language Models
• Distributing Accountability, Not Capability: Phase Separation and the LLM Workflow Quadrant in Autonomous AI Agent Architectures
• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
• Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
• Decision Transformer: Reinforcement Learning via Sequence Modeling
• Reactive Long Horizon Task Execution via Visual Skill and Precondition Models
• A Formal Analysis and Taxonomy of Task Allocation in Multi-Robot Systems

Related

• RobotFleet: An Open-Source Framework for Centralized Multi-Robot Task Planning
• COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models
• PIP-LLM: Integrating PDDL-Integer Programming with LLMs for Coordinating Multi-Robot Teams Using Natural Language
• SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models
• Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?