Reducing Mental Workload through On-Demand Human Assistance for Physical Action Failures in LLM-based Multi-Robot Coordination
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.
An overview of our study. As multiple robots act based on planning generated by an LLM, they may fall into a loop state due to action failures. In such cases, the robots request assistance from an operator as needed, and the issue is resolved through remote error resolution.
An overview of REPAIR. (a) The LLM takes a user’s instruction, decomposes it based on robot capabilities and rules, and assigns tasks to each robot. (b) It then generates executable actions, which the robots perform; when failures occur, they request operator assistance. (c) The operator resolves the issue remotely and provides feedback, allowing the LLM to update its understanding and resume execution.
Video Demonstration (Full)
BibTeX
@inproceedings{hasegawa2026repair,
title={{Reducing Mental Workload through On-Demand Human Assistance for Physical Action Failures in LLM-based Multi-Robot Coordination}},
author={Hasegawa, Shoichi and Taniguchi, Akira and El Hafi, Lotfi and Garcia Ricardez, Gustavo Alfonso and Taniguchi, Tadahiro},
booktitle={{IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)}},
year={2026, under review}
}