Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies
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来源：arXiv cs.RO发布时间待核实
arXiv:2605.00416v3 Announce Type: replace Abstract: 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 datasets cannot fully capture.