AIBullisharXiv – CS AI · 11h ago7/10
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From Discrete Plans to Real-World Execution: A World-Model-Driven Framework for Execution-Aware Multi-Agent Path Finding
Researchers present ExecTimeNet, a learned world model that bridges the gap between discrete multi-agent path finding (MAPF) planning and real-world robot execution by predicting how planned paths perform on physical systems with realistic dynamics and delays. The framework includes REMAP, which integrates execution-time estimation into planning, and ESADG, a post-planning optimizer that achieves up to 40% improvement in execution efficiency while maintaining path feasibility.