用大模型统一调度异构机器人集群，实现多任务协同。
RobotFleet: An Open-Source Framework for Centralized Multi-Robot Task Planning
- 分层模块化设计，用大模型处理开放世界推理
- 支持容器化部署，可扩展管理大规模机器人集群
- 适合研究多机器人协同与系统集成的开发者
协调异构机器人集群完成多项任务在多机器人系统中极具挑战。我们提出一个开源可扩展的集中式多机器人任务规划与调度框架——RobotFleet，利用大语言模型（LLM）使异构机器人集群能够完成多任务。该框架将规划、调度与执行抽象为容器化服务，支持集群的弹性扩展与统一管理。系统维护共享的声明式世界状态，并实现任务执行与重规划之间的双向通信。通过模块化自主栈并结合大模型进行开放世界推理，显著降低构建可扩展多机器人系统的技术门槛。代码已公开：https://github.com/therohangupta/robot-fleet。
原文摘要 · Abstract (English)
Coordinating heterogeneous robot fleets to achieve multiple goals is challenging in multi-robot systems. We introduce an open-source and extensible framework for centralized multi-robot task planning and scheduling that leverages LLMs to enable fleets of heterogeneous robots to accomplish multiple tasks. RobotFleet provides abstractions for planning, scheduling, and execution across robots deployed as containerized services to simplify fleet scaling and management. The framework maintains a shared declarative world state and two-way communication for task execution and replanning. By modularizing each layer of the autonomy stack and using LLMs for open-world reasoning, RobotFleet lowers the barrier to building scalable multi-robot systems. The code can be found here: https://github.com/therohangupta/robot-fleet.
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