CoDiMAD: Diffusion-Based Privileged Distillation for Communication-Free Multi-Robot Coordination
Overview
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.
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- 2026-11-09T22:59:43.753Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalCoDiMAD: 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.
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