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A decentralized Perception-Action-Communication system for multi-robot teams that enables them to collaborate in large scale, outdoor environments and upholds crucial properties for field-deployable robot collectives: scalability, resiliency and repeatability.
This work proposes COMPASS, a scalable, decentralized multi-robot architecture for controlling large collectives of agentic robots with reasoning space feedback control, and shows that reasoning feedback works best when composed with a compact learned token.
A theoretical framework for GNN transferability over sparse Random Geometric Graphs (RGGs) is established, capturing distance-dependent channel decay and spatial interference and extending this framework to conflict graph models, deriving equivalent scalability guarantees for link-level resource allocation policies.
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