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Neural-ESO achieves unprecedented operational reliability in learning-based control by balancing predictive neural networks with traditional error correction methods.
Multi-robot systems can now better handle perception uncertainty and visibility constraints by adapting safety bounds based on formation risk, leading to improved success rates and tracking accuracy.
