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Learning Communication-Conditioned Generative Policies for Decentralized Multi-Agent Collision Avoidance

arXiv · AI, language, vision and robotics · article · Sep 13, 2026 · UTC

In this work, we propose a decentralized communication-conditioned generative framework for multi-agent collision avoidance. Agents generate short-horizon action sequences using a flow-matching policy trained from privileged offline demonstrations with access to global state. The demonstrations do not include explicit communication signals; instead, agents learn to exchange and aggregate latent messages that encode interaction-relevant intent under partial observability. This formulation supports flexible inference at test time, where unconditioned generation corresponds to independent behavio

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