A Closed-Loop Multi-Agent Framework for Robust Multi-Robot Manipulation

• URL: http://arxiv.org/abs/2607.06990v1
• Date: Wed, 08 Jul 2026 04:23:41 GMT
• Title: A Closed-Loop Multi-Agent Framework for Robust Multi-Robot Manipulation
• Abstract summary: Large Language Models (LLMs) offer the reasoning capabilities to decompose objectives into actionable plans.<n>We propose a hierarchical closed-loop agentic framework to ensure robust multi-robot manipulation.<n>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.
• Score: 37.78546236286294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Bridging Thought and Action: Taming Long-Horizon Instability in Open-Source LLM Agents with a MetaTool-Enhanced ROS Framework [0.0]
  Large Language Models (LLMs) have enabled more natural human-robot interaction, but open-source models exhibit unstable long-horizon reasoning.<n>This paper presents an enhanced ROS-Agent based architecture that improves task reliability and execution efficiency.
  arXiv  Detail & Related papers  (2026-09-11T10:00:49Z)
• Beyond Flat Policies: Hierarchical Post-Training for Embodied Agents in Robotic Manipulation [34.87301719899886]
  Vision-language-action (VLA) models have demonstrated remarkable capabilities in robotic manipulation by leveraging pretrained vision-language models.<n>Existing post-training methods predominantly optimize VLA models as flat policies, making it difficult to explicitly model task progression and perform robust long-horizon manipulation.<n>We propose Hierarchical Robotic Control (HiRoC), a hierarchical post-training framework that decouples high-level task planning from low-level action execution.
  arXiv  Detail & Related papers  (2026-08-06T13:07:56Z)
• Multi-Agent Tool-Integrated Policy Optimization [67.12841355267678]
  Large language models (LLMs) increasingly rely on multi-turn tool-integrated planning for knowledge-intensive and complex reasoning tasks.<n>Existing implementations typically rely on a single agent, but they suffer from limited context length and noisy tool responses.<n>No existing methods support effective reinforcement learning post-training of tool-integrated multi-agent frameworks.
  arXiv  Detail & Related papers  (2025-10-06T10:44:04Z)
• MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation [62.854649499866774]
  Large Language Models (LLMs) have demonstrated remarkable planning abilities across various domains, including robotics manipulation and navigation.<n>We propose a novel multi-agent LLM framework that distributes high-level planning and low-level control code generation across specialized LLM agents.<n>We evaluate our approach on nine RLBench tasks, including long-horizon tasks, and demonstrate its ability to solve robotics manipulation in a zero-shot setting.
  arXiv  Detail & Related papers  (2024-11-26T17:53:44Z)
• EMOS: Embodiment-aware Heterogeneous Multi-robot Operating System with LLM Agents [33.77674812074215]
  We introduce a novel multi-agent framework designed to enable effective collaboration among heterogeneous robots.<n>We propose a self-prompted approach, where agents comprehend robot URDF files and call robot kinematics tools to generate descriptions of their physics capabilities.<n>The Habitat-MAS benchmark is designed to assess how a multi-agent framework handles tasks that require embodiment-aware reasoning.
  arXiv  Detail & Related papers  (2024-10-30T03:20:01Z)
• Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
  We propose AOP, a novel framework for agent-oriented planning in multi-agent systems.<n>In this study, we identify three critical design principles of agent-oriented planning, including solvability, completeness, and non-redundancy.<n> Extensive experiments demonstrate the advancement of AOP in solving real-world problems compared to both single-agent systems and existing planning strategies for multi-agent systems.
  arXiv  Detail & Related papers  (2024-10-03T04:07:51Z)
• COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models [49.24666980374751]
  COHERENT is a novel LLM-based task planning framework for collaboration of heterogeneous multi-robot systems.<n>A Proposal-Execution-Feedback-Adjustment mechanism is designed to decompose and assign actions for individual robots.<n>The experimental results show that our work surpasses the previous methods by a large margin in terms of success rate and execution efficiency.
  arXiv  Detail & Related papers  (2024-09-23T15:53:41Z)
• CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces [9.578169216444813]
  This paper proposes an approach to coordinating multi-robot manipulation through learned latent action spaces that are shared across different agents. We validate our method in simulated multi-robot manipulation tasks and demonstrate improvement over previous baselines in terms of sample efficiency and learning performance.
  arXiv  Detail & Related papers  (2022-11-28T23:20:47Z)
• Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints [52.58352707495122]
  We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination. We validate our results over a wide range of simulations on two distinct domains: multi-arm conveyor belt pick-and-place and multi-drone delivery dispatch in a city.
  arXiv  Detail & Related papers  (2020-05-27T01:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.