用于鲁棒多机器人操作的闭环多智能体框架
A Closed-Loop Multi-Agent Framework for Robust Multi-Robot Manipulation
AI总结：
针对多机器人操作中高级推理应用于实际执行的挑战，提出基于分层闭环智能体的LLM框架，含规划、操作、验证智能体。经实际实验验证，该框架成功率高、适应性强，为多样操作任务提供可推广方法。
摘要：
多机器人系统为长期任务提供了并行性和冗余性，而大语言模型（LLMs）提供了将目标分解为可执行计划的推理能力。然而，将这种高级推理有效地应用于物理多机器人执行仍然是一个开放的挑战。现有的基于LLM的方法主要分为两类：单机器人方法实现了强大的富含接触的操作，但缺乏跨多个工作空间任务所需的协调机制。当前的多机器人框架侧重于高级规划，往往将操作视为理想化的原语，无法考虑现实世界执行中的不确定性。为了解决这个问题，我们提出了一个基于分层闭环智能体的LLM框架，以确保鲁棒的多机器人操作。我们的系统由三个专门的智能体组成：规划智能体将指令分解为分配的子任务，每个机器人的操作智能体通过自适应工具使用来执行动作，验证智能体通过监测物理结果并反馈语义校正来闭环。大量的实际实验表明，我们的框架实现了更高的成功率，确保了从单工作空间到跨工作空间操作的强大适应性，并为各种操作任务提供了一种可推广的方法。
英文摘要：
Multi-robot systems provide the parallelism and redundancy necessary for long-horizon tasks, while Large Language Models (LLMs) offer the reasoning capabilities to decompose these objectives into actionable plans. However, effectively grounding this high-level reasoning in physical multi-robot execution remains an open challenge. Existing LLM-based approaches fall mainly into two categories: Single-robot methods achieve robust contact-rich manipulation but lack the coordination mechanisms required for tasks spanning multiple workspaces. Current multi-robot frameworks focus on high-level planning, often treating manipulation as an idealized primitive that fails to account for real-world execution uncertainties. To address this, we propose a hierarchical closed-loop agentic LLM-based framework to ensure robust multi-robot manipulation. Our system consists of three specialized agents: the Planning Agent decomposes instructions into allocated sub-tasks, the Manipulation Agent for each robot executes actions via adaptive tool use, and the Verification Agent closes the loop by monitoring physical outcomes and feeding back semantic corrections. Extensive real-world experiments demonstrate that our framework achieves superior success rates, ensures robust adaptability ranging from single to cross workspace manipulation, and offers a generalizable approach for diverse manipulation tasks.
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。
