DuoMind: Enabling Distributed Multi-Robot Coordination with Semantic Communication

Hanchu Zhou, Dechen Gao, Hang Wang, Brendan Lynch, Boqi Zhao, Qiyao Ma, Raman Goyal, Junshan Zhang · N/A · 2026

Summary

Vision-language models (VLMs) and vision-language-action models (VLAs) have recently driven rapid progress in general-purpose robots, yet most progress has focused on single-robot settings.

Abstract Summary

Vision-language models (VLMs) and vision-language-action models (VLAs) have recently driven rapid progress in general-purpose robots, yet most progress has focused on single-robot settings. Extending these capabilities to multi-robot systems remains challenging because robots must coordinate long-horizon behaviors while maintaining reliable, fine-grained execution. We introduce DuoMind, a distributed hierarchical framework for multi-robot coordination through semantic communication. Each robot uses a VLA-based action model for low-level execution and a VLM-based orchestrator for high-level reasoning and inter-agent coordination. At each planning step, the orchestrator at each robot reasons over the task instruction, local observations, and messages received from other robots. It then generates low-level instructions for the action model and semantic messages for peer robots. This architecture exploits the complementary strengths of pretrained models by combining the semantic reasoning capabilities of VLMs with the precise action-generation capabilities of VLAs. To address the scarcity of benchmarks for multi-robot coordination, we further develop RoboPoly, a benchmark comprising long-horizon manipulation tasks that require coordinated, closed-loop execution under distributed control. Experiments on RoboPoly and RoboTwin demonstrate that DuoMind improves multi-robot task performance, while ablation studies confirm the contributions of hierarchical orchestration and semantic communication. More details are available on our project page.

Key Points

Vision-language models (VLMs) and vision-language-action models (VLAs) have recently driven rapid progress in general-purpose robots, yet most progress has focused on single-robot settings.
Extending these capabilities to multi-robot systems remains challenging because robots must coordinate long-horizon behaviors while maintaining reliable, fine-grained execution.
We introduce DuoMind, a distributed hierarchical framework for multi-robot coordination through semantic communication.
Each robot uses a VLA-based action model for low-level execution and a VLM-based orchestrator for high-level reasoning and inter-agent coordination.
At each planning step, the orchestrator at each robot reasons over the task instruction, local observations, and messages received from other robots.

DuoMind : Distributed Hierarchical Multi-Agent Framework

We introduce DuoMind , a distributed hierarchical framework for multi-agent robotic coordination. We first present the VLM-based orchestrator for high-level planning ( 2.1 ). We then describe how we bridges high-level planning and low-level control via embodied reasoning and agentic harness ( 2.2 ). Finally, we highlight the structured inter-agent communication ( 2.3 ).

RoboPoly

RoboPoly is designed to facilitate research on multi-robot manipulation by providing challenging long-horizon tasks with explicit cooperation requirements. Unlike RoboTwin Chen et al.

(2025c) , which primarily targets dual-arm manipulation with two arms on the same embodiment and does not necessarily require explicit cooperation, RoboPoly considers two independent robots and tasks specifically designed around multi-robot coordination.

RoboPoly — DuoMind: Enabling Distributed Multi-Robot Coordination with Semantic Communication — Figure 3: We observe that explicit inter-agent communication enables the robots to better coordinate their behaviors, such as exchanging bread sequentially or placing objects on different plates. By avoiding conflicting or redundant actions in a narrow shared workspace, the robots achieve more efficient and safer coordination.

Experiments

We evaluate DuoMind on 7 tasks from our RoboPoly and 8 dual-arm tasks from RoboTwin 2.0 ( Chen et al., 2025c ) , both under distributed control. Since RoboTwin originally adopts centralized control over the dual-arm robot, we adapt its setting by treating the two arms as independent robots and control them separately. Task overview are shown in fig. 5 and fig. 6 .

All the finetuning and evaluation tasks are conducted on 4 NVIDIA A100 GPU, each taking 10–24 hours.

Experiments — DuoMind: Enabling Distributed Multi-Robot Coordination with Semantic Communication — (a) Inter-agent communication ablation.

Sources and Demonstrations

Figures are reproduced from Zhou et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.