CoDiMAD: Diffusion-Based Privileged Distillation for Communication-Free Multi-Robot Coordination
CoDiMAD: Diffusion-Based Privileged Distillation for Communication-Free Multi-Robot Coordination
This research presents CoDiMAD, a framework that uses conditional denoising diffusion probabilistic models to distill knowledge from a globally informed oracle to decentralized robot agents. The method addresses the challenge of multi-modal action distributions in partially observable environments by avoiding mode averaging inherent in deterministic distillation. Experiments demonstrate consistent performance improvements over direct local multi-agent reinforcement learning and deterministic baselines across three cooperative tasks.
The paper introduces CoDiMAD, a diffusion-based method for multi-robot coordination that addresses mode collapse in deterministic distillation by sampling from coherent action distributions; results are reported on three cooperative tasks with source code pending release upon acceptance.
Source: arXiv Robotics — research abstracts · Read original article ↗
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de-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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Source：arXiv Robotics — research abstracts · arxiv.org