LLM2Swarm：通过大语言模型实现响应式推理、规划与协作的机器人群
LLM2Swarm: Robot Swarms that Responsively Reason, Plan, and Collaborate through LLMs
AI总结：
针对机器人群控制器定制耗时易错、难应对突发情况的问题，提出LLM2Swarm框架，探索间接集成（LLM合成验证控制器）与直接集成（机器人本地运行LLM协作）两种方案，验证了其异常检测等能力并开源系统。
摘要：
机器人群由大量简单机器人组成，它们通过通信协作完成复杂任务。机器人控制器通常需要专家针对具体场景通过编程代码逐一指定，这一过程耗时、易出错，且无法覆盖部署过程中可能遇到的所有情况。另一方面，近期的大语言模型（Large Language Models, LLMs）已展现出推理与规划能力，为机器交互与编程提供了新方式，还融合了领域特定知识与常识知识。因此，我们提出将LLMs与机器人群集成以解决上述挑战，并通过概念验证（展示案例）证明了其潜力。\n针对该集成方案，我们探索了两种方法。第一种是“间接集成”，即利用LLMs合成并验证机器人控制器。该方法可减少部署前的开发时间与人为错误，此外在部署期间还可用于即时创建新的机器人行为。第二种是“直接集成”，即部署期间每个机器人在本地运行独立的LLM实例，用于机器人间协作与人机群交互。这些本地LLM实例让每个机器人能够使用自然语言进行推理、规划与协作，我们的展示案例证明，机器人无需提前获知异常类型的相关信息，就能检测多种异常。为推动对这一主要为概念性贡献的后续研究，我们发布了LLM2Swarm系统的软件与演示视频，访问地址为：https://github.com/Pold87/LLM2Swarm。
英文摘要：
Robot swarms are composed of many simple robots that communicate and collaborate to fulfill complex tasks. Robot controllers usually need to be specified by experts on a case-by-case basis via programming code. This process is time-consuming, prone to errors, and unable to take into account all situations that may be encountered during deployment. On the other hand, recent Large Language Models (LLMs) have demonstrated reasoning and planning capabilities, introduced new ways to interact with and program machines, and incorporate both domain-specific and commonsense knowledge. Hence, we propose to address the aforementioned challenges by integrating LLMs with robot swarms and show the potential in proofs of concept (showcases). For this integration, we explore two approaches. The first approach is 'indirect integration,' where LLMs are used to synthesize and validate the robot controllers. This approach may reduce development time and human error before deployment. Moreover, during deployment, it could be used for on-the-fly creation of new robot behaviors. The second approach is 'direct integration,' where each robot locally executes a separate LLM instance during deployment for robot-robot collaboration and human-swarm interaction. These local LLM instances enable each robot to reason, plan, and collaborate using natural language, as demonstrated in our showcases where the robots are able to detect a variety of anomalies, without prior information about the nature of these anomalies. To enable further research on our mainly conceptual contribution, we release the software and videos for our LLM2Swarm system: https://github.com/Pold87/LLM2Swarm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
