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Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies
Generalist robot policies increasingly benefit from large-scale pretraining, but offline data alone is insufficient for robust real-world deployment. Deployed robots encounter distribution shifts, long-tail failures, task variations, and human correction opportunities that fixed demonstration datase
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