With the increasing prevalence and diversity of robots interacting in the real world, there is need for flexible, on-the-fly planning and cooperation. Large Language Models are starting to be explored in a multimodal setup for communication, coordination, and planning in robotics. Existing approaches generally use a single agent building a plan, or have multiple homogeneous agents coordinating for a simple task. We present a decentralised, dialogical approach in which a team of agents with different abilities plans solutions through peer-to-peer and human-robot discussion. We suggest that argument-style dialogues are an effective way to facilitate adaptive use of each agent's abilities within a cooperative team. Two robots discuss how to solve a cleaning problem set by a human, define roles, and agree on paths they each take. Each step can be interrupted by a human advisor and agents check their plans with the human. Agents then execute this plan in the real world, collecting rubbish from people in each room. Our implementation uses text at every step, maintaining transparency and effective human-multi-robot interaction.

翻译：随着真实世界中交互机器人的普及和多样化发展，灵活且动态的规划与合作需求日益凸显。大型语言模型在多模态情境下正被探索用于机器人的通信、协调与规划。现有方法通常采用单一智能体构建规划，或由多个同质智能体为简单任务进行协作。本文提出一种去中心化的对话式方法，使具备不同能力的智能体团队通过点对点及人机讨论来规划解决方案。我们认为，论证式对话是促进协作团队中自适应运用各智能体能力的有效途径。两台机器人围绕人类设定的清洁问题进行讨论，明确角色分工并协商各自行进路径。每个步骤均可由人类顾问中断，智能体需与人类核验规划内容。随后智能体在真实世界执行该规划，从各房间人员处收集垃圾。我们的实现方案全程基于文本，保持透明度与高效的人机多机器人交互。