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ARMADA introduces a system for scalable real-world deployment and rapid adaptation of visuomotor robot policies by integrating an autonomous failure detector and a multi-robot shared control framework. The system achieves nearly 95% failure detection accuracy and reduces human intervention rates by 23.3%, significantly improving policy robustness and adaptation efficiency.

A modal-level exploration framework enables robotic policies to autonomously improve beyond initial human demonstrations through noise injection in the model's reasoning space, demonstrating enhanced performance on manipulation tasks while maintaining sample efficiency through selective data filtering and policy fine-tuning strategies.