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Robots can learn from fleet-wide failures and successes to autonomously improve their policies, achieving near-perfect reliability without any new human corrections.
Each evaluation run in EVA-Client not only assesses performance but also enriches the training dataset, creating a continuous feedback loop for policy improvement.
A novel framework enables zero-shot localization across ground and drone views, outperforming traditional methods by leveraging a rich dataset and advanced geometric modeling.