用自然语言指令自动生成可部署的多机器人控制策略
GenSwarm: Scalable Multi-Robot Code-Policy Generation and Deployment via Language Models
- 基于大模型实现自然语言到代码策略的端到端生成
- 零样本学习支持动态任务快速适应，无需重写目标函数
- 代码策略可解释且可复现，适合机器人专家与非专家使用
多机器人系统控制策略的开发传统上复杂且耗时，难以应对动态任务。现有自动方法需反复人工设计和优化目标函数，延长开发周期。本文提出GenSwarm，一个基于大语言模型的端到端系统，仅需用户自然语言指令即可自动生成并部署多机器人控制策略。作为多语言智能体系统，GenSwarm具备零样本学习能力，可快速适应新任务或变更任务。其白盒代码策略保证强可复现性与可解释性。通过可扩展的软硬件架构，系统可在仿真与真实多机器人平台上高效部署，实现从指令到执行的全流程自动化，对机器人研究人员及非专业用户均具实用价值。代码已开源：https://github.com/WindyLab/GenSwarm。
原文摘要 · Abstract (English)
The development of control policies for multi-robot systems traditionally follows a complex and labor-intensive process, often lacking the flexibility to adapt to dynamic tasks. This has motivated research on methods to automatically create control policies. However, these methods require iterative processes of manually crafting and refining objective functions, thereby prolonging the development cycle. This work introduces \textit{GenSwarm}, an end-to-end system that leverages large language models to automatically generate and deploy control policies for multi-robot tasks based on simple user instructions in natural language. As a multi-language-agent system, GenSwarm achieves zero-shot learning, enabling rapid adaptation to altered or unseen tasks. The white-box nature of the code policies ensures strong reproducibility and interpretability. With its scalable software and hardware architectures, GenSwarm supports efficient policy deployment on both simulated and real-world multi-robot systems, realizing an instruction-to-execution end-to-end functionality that could prove valuable for robotics specialists and non-specialists alike.The code of the proposed GenSwarm system is available online: https://github.com/WindyLab/GenSwarm.
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