closing-the-lab-to-store-gap-a-data-efficient-post

name	closing-the-lab-to-store-gap-a-data-efficient-post
description	Skill generated from arXiv paper 2607.20345: Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids
metadata	{"arxiv":{"id":"2607.20345","title":"Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids","authors":["Roger Sala Sisó","Tiago Silvério","Jakob Sand","Tran Nguyen Le"],"published":"2026-07-22","categories":["cs.RO","cs.AI"],"url":"https://arxiv.org/abs/2607.20345","utility":0.93}}

Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids

arXiv: 2607.20345
Published: 2026-07-22
Authors: Roger Sala Sisó, Tiago Silvério, Jakob Sand, Tran Nguyen Le
Categories: cs.RO, cs.AI
Utility: 0.93

Key Innovation

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 Unitree G1-Edu humanoid robot and the GR00T N1.6 foundation model. DEED comprises thr...

Potential Application

This paper presents advancements that could be applied to enhance agent capabilities in the areas of cs.RO, cs.AI.

References

• arXiv: https://arxiv.org/abs/2607.20345