arXiv: cs.AI preprintsInternational6 November 2026
EmboTeam: Asymmetric State Grounding for Heterogeneous Multi-Robot Collaboration
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arXiv:2601.11063v3 Announce Type: replace-cross Abstract: Heterogeneous multi-robot collaboration over long horizons requires both global task planning and robust coordination during execution. Existing plan- ners using large language models (LLMs) can generate task decompositions or symbolic plans, but often execute them as rigid action sequences, making them fragile to local state mismatches and synchronization failures. We propose Em- boTeam, a neuro-symbolic framework for asymmetric state grounding. An LLM generates structured state expectations, which are verified and coordinated through
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