Reducing Mental Workload through On-Demand Human Assistance for Physical Action Failures in LLM-based Multi-Robot Coordination

• URL: http://arxiv.org/abs/2603.28156v1
• Date: Mon, 30 Mar 2026 08:21:04 GMT
• Title: Reducing Mental Workload through On-Demand Human Assistance for Physical Action Failures in LLM-based Multi-Robot Coordination
• Abstract summary: In this study, we propose a human-in-the-loop framework that integrates remote error resolution into multi-robot planning.<n>In this method, robots execute tasks autonomously; however, when an irrecoverable failure occurs, the LLM requests assistance from an operator.<n>For easily collectable items, it achieved task progress equivalent to full remote control.
• Score: 6.763690463901024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-robot coordination based on large language models (LLMs) has attracted growing attention, since LLMs enable the direct translation of natural language instructions into robot action plans by decomposing tasks and generating high-level plans. However, recovering from physical execution failures remains difficult, and tasks often stagnate due to the repetition of the same unsuccessful actions. While frameworks for remote robot operation using Mixed Reality were proposed, there have been few attempts to implement remote error resolution specifically for physical failures in multi-robot environments. In this study, we propose REPAIR (Robot Execution with Planned And Interactive Recovery), a human-in-the-loop framework that integrates remote error resolution into LLM-based multi-robot planning. In this method, robots execute tasks autonomously; however, when an irrecoverable failure occurs, the LLM requests assistance from an operator, enabling task continuity through remote intervention. Evaluations using a multi-robot trash collection task in a real-world environment confirmed that REPAIR significantly improves task progress (the number of items cleared within a time limit) compared to fully autonomous methods. Furthermore, for easily collectable items, it achieved task progress equivalent to full remote control. The results also suggested that the mental workload on the operator may differ in terms of physical demand and effort. The project website is https://emergentsystemlabstudent.github.io/REPAIR/.

Related papers

• D-VLC: Decentralized Vision-Language Collaboration for Heterogeneous Embodied Multi-Robot Systems in Unknown Environments [10.393866368123946]
  Multi-robot systems can improve the efficiency of complex task execution through parallel collaboration and complementary capabilities.<n>We propose a framework that combines decentralized asynchronous reasoning, lightweight information sharing, capability aware collaboration, and a unified action interface.<n>Experiments across diverse scenarios and multiple VLMs show success rates above 70%, with completion time reduced by up to 55.8% relative to the geometric greedy baseline.
  arXiv  Detail & Related papers  (2026-07-31T04:20:32Z)
• A Closed-Loop Multi-Agent Framework for Robust Multi-Robot Manipulation [37.78546236286294]
  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.
  arXiv  Detail & Related papers  (2026-07-08T04:23:41Z)
• CORAL: Scalable Multi-Task Robot Learning via LoRA Experts [49.759823970016974]
  Vision-Language-Action (VLA) models in real-world robotics expose a core multi-task learning challenge.<n>Maintaining a separate full checkpoint per task is often storage- and deployment-prohibitive.<n>We present CORAL, a backbone- and-agnostic framework designed primarily to mitigate multi-task interference.
  arXiv  Detail & Related papers  (2026-03-10T07:28:41Z)
• From Dialogue to Execution: Mixture-of-Agents Assisted Interactive Planning for Behavior Tree-Based Long-Horizon Robot Execution [10.513251543236684]
  Large language models (LLMs) enable robots to generate high-level action plans from natural language instructions.<n>In long-horizon tasks, such approaches often require many questions, increasing user burden.<n>We propose a framework that integrates Mixture-of-Agents (MoA)-based proxy answering into interactive planning.
  arXiv  Detail & Related papers  (2026-03-01T13:54:51Z)
• REMAC: Self-Reflective and Self-Evolving Multi-Agent Collaboration for Long-Horizon Robot Manipulation [57.628771707989166]
  We propose an adaptive multi-agent planning framework, termed REMAC, that enables efficient, scene-agnostic multi-robot long-horizon task planning and execution.<n>ReMAC incorporates two key modules: a self-reflection module performing pre-conditions and post-condition checks in the loop to evaluate progress and refine plans, and a self-evolvement module dynamically adapting plans based on scene-specific reasoning.
  arXiv  Detail & Related papers  (2025-03-28T03:51:40Z)
• Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks.<n>However, they still struggle with problems requiring multi-step decision-making and environmental feedback.<n>We propose a framework that can automatically learn a reward model from the environment without human annotations.
  arXiv  Detail & Related papers  (2025-02-17T18:49:25Z)
• 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)
• 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)
• Large Language Models for Orchestrating Bimanual Robots [19.60907949776435]
  We present LAnguage-model-based Bimanual ORchestration (LABOR) to analyze task configurations and devise coordination control policies. We evaluate our method through simulated experiments involving two classes of long-horizon tasks using the NICOL humanoid robot.
  arXiv  Detail & Related papers  (2024-04-02T15:08:35Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers [20.857692296678632]
  For effective human-robot interaction, robots need to understand, plan, and execute complex, long-horizon tasks. Recent advances in large language models have shown promise for translating natural language into robot action sequences. We show that our approach outperforms several methods using LLMs as planners in complex task domains.
  arXiv  Detail & Related papers  (2023-06-10T21:58:29Z)

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.