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arXiv · 2607.09587

CoDiMAD: Diffusion-Based Privileged Distillation for Communication-Free Multi-Robot Coordination

Jiyue Tao·Shunheng Xin·Tongsheng Shen·Dexin Zhao·Feitian Zhang
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Abstract

Decentralized multi-robot coordination under partial observability remains challenging, especially in communication-free settings where agents must act solely from local sensor observations. Privileged policy distillation provides a promising approach by transferring knowledge from a globally informed oracle to sensor-constrained students. However, in multi-agent systems, the same local observation may correspond to multiple global configurations requiring qualitatively different cooperative actions, making the conditional action distribution inherently multi-modal. Standard deterministic distillation collapses these modes to their mean, often yielding invalid or hesitant actions. To address this issue, we propose CoDiMAD, a three-stage framework that trains a privileged oracle with MAPPO, constructs an offline dataset of local-observation-oracle-action pairs, and distills the oracle into decentralized students parameterized as conditional denoising diffusion probabilistic models. By approximating the conditional oracle-action distribution through the diffusion reverse process, CoDiMAD samples decisive actions from coherent coordination modes rather than averaging across them. Theoretical analysis characterizes the mode-averaging failure of deterministic distillation and the distributional recovery property of diffusion-based distillation. Experiments on three cooperative tasks show that CoDiMAD consistently outperforms direct local MARL and deterministic distillation baselines. The source code will be made publicly available upon acceptance.

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Jiyue Tao, Shunheng Xin, Tongsheng Shen, Dexin Zhao, Feitian Zhang. 2026-07-10. CoDiMAD: Diffusion-Based Privileged Distillation for Communication-Free Multi-Robot Coordination. https://arxiv.org/abs/2607.09587

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