Systematic Multi-Agent Vision-and-Language Navigation: Formulation, Benchmark, and Method

Researchers present Systematic Multi-Agent Vision-and-Language Navigation as a constrained coordination problem for teams of robots. They introduce the MAVLN benchmark, featuring 11,724 episodes across 145 scenes, and TRISS, a navigation system utilizing an LLM-based scheduler, shared topological memory, and a conflict-aware execution mechanism.