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Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids
Closing the gap between benchmark performance and reliable real world operation remains a central challenge for Vision Language Action (VLA) humanoid robots, which must handle execution errors, distribution shifts, and environmental variability. This paper presents DEED (Data Efficient Post Training and Experience Driven Learning), a systems level approach evaluated on a supermarket chip restocking task using a Un...