DynaHMRC: Decentralized Heterogeneous Multi-Robot Collaboration for Dynamic Tasks with Large Language Models
This paper proposes DynaHMRC, a decentralized framework where each robot acts as a role-aware LLM agent, enabling flexible collaboration through a four-stage closed-loop process: self-description, task allocation with leadership bidding, leader election, and reflective execution. It addresses scalability issues of centralized LLM schedulers, insufficient handling of dynamic tasks, and domain-specific data scarcity. Experiments show higher success rates and efficiency compared to strong baselines, with promising scalability.
[2606.14882] DynaHMRC: Decentralized Heterogeneous Multi-Robot Collaboration for Dynamic Tasks with Large Language Models
[Submitted on 12 Jun 2026]
Title:DynaHMRC: Decentralized Heterogeneous Multi-Robot Collaboration for Dynamic Tasks with Large Language Models
View a PDF of the paper titled DynaHMRC: Decentralized Heterogeneous Multi-Robot Collaboration for Dynamic Tasks with Large Language Models, by Wenhao Yu and 7 other authors
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Abstract:Large language models (LLMs) provide robots with richer task understanding and adaptability, making them promising for coordinating heterogeneous multi-robot systems in long-horizon tasks. Despite this potential, several challenges remain underexplored: (1) Centralized LLM schedulers scale poorly as team size and environmental complexity increase. A single model must process excessive contextual information, and long-context approximation may degrade reasoning quality; (2) Existing task formulations insufficiently consider dynamic settings, while robust adaptation to evolving task conditions is essential for real-world deployment; (3) Domain-specific data scarcity limits specialized robotic reasoning, making proprietary general-purpose models inefficient for expert tasks. To address these limitations, we propose DynaHMRC, a decentralized framework in which each robot acts as a role-aware LLM agent. This design mitigates the single-model context bottleneck and supports flexible collaboration across heterogeneous team configurations. DynaHMRC organizes collaboration as a four-stage closed-loop process: self-description, task allocation with leadership bidding, leader election, and reflective execution, supported by executable robot interfaces. We further develop a benchmark covering three task families, four dynamic variations, and six team configurations to systematically study dynamic task modeling. In addition, we conduct an empirical analysis to guide the construction of domain-specific expert datasets and fine-tune pretrained LLMs to improve specialized competence. Experiments show that DynaHMRC achieves higher success rates than strong baselines with fewer action and communication steps, while demonstrating promising scalability trends as team size grows within the evaluated settings.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.14882 [cs.RO]
(or arXiv:2606.14882v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.14882
arXiv-issued DOI via DataCite
Submission history
From: Wenhao Yu [view email] [v1] Fri, 12 Jun 2026 18:41:30 UTC (13,151 KB)
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